SciScore for 10.1101/2022.05.27.493693: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22273991: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.26.22275641: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.493484: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275614: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

These mechanisms for collaboration and communication should also be tested using robust simulation exercises that could help identify weaknesses, ambiguities, and bottlenecks [31]. This study and its results suffer from several limitations. First, and most significantly, the results relied on the completion of a survey by one individual. While we attempted to distribute the survey to the local authority who would be most knowledgeable about epidemic and pandemic preparedness in the city, we cannot guarantee that the data captured by responses are completely valid or factual. One person’s knowledge regarding all of the preparedness efforts, activities, and arrangements in their city may be limited. For instance, a majority of survey participants had served in their current professional role for one to four years and may not have been aware of preparedness efforts in their city before this time. Future research may wish to more deeply examine preparedness efforts in specific cities or to validate the results of this study by reviewing the legislation, regulations, and other legal frameworks that provide the foundation for public health preparedness and coordination between levels of government. Additionally, the specific results from each city are unlikely to hold great amounts of external validity. The governance contexts in which participating cities exist vary widely, and it would be inappropriate to generalize results across contexts without examining the specific authoriti...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275599: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.26.493517: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275467: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

A limitation of this study is that includes a small, heterogeneous sample. While this study aims to capture the real-life efficacy of T+C, the true effect of T/C is difficult to capture due to confounding factors from varying vaccination timeline, SARS-CoV-2 infection status, and other monoclonal antibody injections. Another limitation is the lack of long-term follow-up to assess rates of clinical breakthrough and safety. The study relied on participant surveys sent out a week after T+C doses and unsolicited patient self-reported to assess the safety and clinical breakthrough. Considering that preliminary safety and breakthrough data revealed events months after injections, future studies should be conducted on the long-term safety and breakthrough rate of T+C, particularly in SOTRs as their interaction with T+C has been understudied. T+C is a reasonable complementary strategy to vaccination in high-risk SOTRs to improve neutralizing capacity against select Omicron sublineages. Assessment of the real-world efficacy and safety of T+C in SOTR is of utmost importance, since SOTRs face heightened risks of severe illness or death from COVID-19 and since only few SOTRs were included in the PROVENT trial. Studies such as ours incorporating both clinical and laboratory data offer valuable insights into the real-world efficacy and safety of T+C in one of our most vulnerable population. Further research examining the durability of neutralization against emerging Omicron sublineages is ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275006: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations: These overuse measures are indicators of low-value care, and are limited by the diagnostic information available in claims data. We used measures from the Lown Institute overuse metric that had a specific denominator cohort.7 We did not include the two measures from this metric that have a denominator of all patients at the hospital over the selected time period (inferior vena caval filters and renal stenting). We adjusted for and investigated state-level beneficiary counts and COVID-19 incidence rates. A more granular approach could have used counts at the hospital referral region or county level and explored within and across region differences. COVID-19 incidence at more local regional levels may be more predictive of overuse or patient volumes than at the state level. This could be future research built from this current study, which presents a more high-level overview of specific denominator and numerator overuse measure rates throughout 2020. This study only investigated the impact on services through 2020. By the end of 2020, the US was entering another COVID-19 surge that lasted most of the winter. Later in 2021, the COVID-19 Omicron variant wave caused another surge in cases and hospitalizations. The changes to denominator and overuse rates of these measures may be different during each pandemic stage. Conclusions: For most investigated overuse measures, we observed the largest decrease in overuse rates during the COVID-19 shutdown period. These were lar...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275586: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

There are several limitations in the included RCTs that should be considered alongside the findings. There was no evidence located for use of non-invasive ventilation strategies in infants or children or important subpopulations (e.g., pregnant women), very limited evidence to support the specific use of named interfaces for the ventilation strategies considered (e.g., nasal cannula, simple face mask, venturi mask, oronasal, helmet or other), and only one RCT reported patient-important outcomes. The hospital LOS data are challenging to interpret as competing risk for death may not have been appropriately accounted for in most RCTs, or it is not possible to assess how competing risk was handled in an RCT66. In the set of included RCTs, the LOS outcomes are generally either secondary or exploratory outcomes, and as such, the estimates presented may be confounded by death. The LOS data for survivors and non-survivors is rarely presented. Patient heterogeneity was noted by Perkins et al. in the pragmatic RECOVERY-RS RCT but not explored in-depth41. Owing to the rapid synthesis approach, there was no formal extraction or synthesis of harm outcomes. When non-invasive ventilation supports are considered, potential benefits must be consiered alongside any conceivable harms. Use of ventilation strategies could delay other clinical intervention that could lead to worsening patient prognosis, cause discomfort to the patient during use, or result in surficial wounds or pressure sores whe...

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04326075</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Early CPAP in COVID-19 Patients With Respiratory Failure.</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04381923</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Withdrawn</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">COVIDNOCHE Trial (HFNO Versus CPAP Helmet) in COVID-19 Pneum…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04507802</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Not yet recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Helmet vs Face Mask in Patients With Acute Respiratory Distr…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04667923</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Noninvasive Ventilation in Moderate-to-severe COVID-19-assoc…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04477668</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Helmet Non-Invasive Ventilation for COVID-19 Patients</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04390191</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Terminated</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Early CPAP in COVID-19 Confirmed or Suspected Patients</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04395807</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Terminated</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Helmet CPAP Versus HFNC in COVID-19</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04655638</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">HFNT vs. COT in COVID-19</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.26.22275631: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Nevertheless, a number of study limitations should be acknowledged. Firstly, residents at the RCHE were excluded from the current analysis because of substantial missing records, complex referral patterns between different levels and categories of treatment facilities and/or prolonged delays in oral antiviral prescription during the peak of this pandemic wave. Further studies are needed to examine the real-world safety and effectiveness of oral antivirals in specific healthcare settings, for instance, nursing homes and residential care facilities. Secondly, indication bias could not be eliminated in the prescription of oral antivirals, as reflected by the considerably older age and lower percentage of patients who had been fully vaccinated among oral antiviral users than matched controls at baseline. Indication bias might also be present in the clinical decision to prescribe molnupiravir versus nirmatrelvir/ritonavir, as the latter could be confounded by its significant drug-drug interactions. After matching, patient characteristics between oral antiviral and respective control groups were well balanced at baseline. Thirdly, some information biases might exist in the collection of data during the peak of this pandemic wave, such as the self-reporting of COVID-19 cases based on positive RAT with varying sensitivity. Lastly, there might have been an underreporting of COVID-19 cases during the study period, and the overwhelmed public healthcare system might have prevented some p...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.26.22275585: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Strengths and limitations: LCSS was informed by theory and other stigma scales, co-designed with people with Long Covid and validated in a large UK sample, and takes less than 10 minutes to complete. This study has two notable limitations. First, the convenience non-probability sampling limits generalisability: university-educated white women from England are over-represented, and this may have resulted in an under- or over-estimation of Long Covid stigma. Women and ethnic minorities may be more stigmatised by other similar conditions such as ME/CFS or fibromyalgia(39), though there is little quantitative evidence to support this. The survey did not include patients hospitalised with COVID-19 in the first two weeks of illness, indicating severe acute disease. Stigma levels could be higher in this group as they may have a higher prevalence of prolonged ongoing symptoms(40), or could be lower due to legitimisation of their illness given by the severity of its acute stage. However, the community sample renders this study unique within a largely clinical evidence-base where diagnostic coding for Long Covid remains patchy and inconsistent due to varying knowledge and the absence of specific guidelines(41). The social media recruitment strategy aimed to include an underrepresented group of people living with Long Covid – those not actively engaged with the healthcare system. Second, stigma is a non-pathological construct and measurements do not have standardised diagnostic criteria...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275300: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275487: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations: Some limitations exist in our study. First, the study mainly focus on the disease transmissibility while not explore the impact of vaccine or seasonal factors on disease severity. Second, the number of total infections may be underestimated since the proportion of cases that are underreported is largely unknown when the testing capacity is limited. Therefore, we attempted to capture the changes in underreporting and reporting delay in our modelling. Third, model validation may be sensitive to the assumption of the protectiveness of vaccine or natural infections. Here the data used in our study were based on a published empirical study without age stratification [7]. Conclusion: A recent work has suggested a striking effect of temperature on the spread of COVID-19 [10]. Here, we found that temperature was associated with a larger impact on the transmissibility than strict public health interventions without lockdown throughout a significant outbreak in a single city. Incorporating seasonal variation in temperature can improve the accuracy of modelling of SARS-CoV-2 transmission, which helps to find a balance between normal life and low health impact.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275592: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The main limitations of this study should be acknowledged. We did not examine the influence of COVID-19 infection on the study outcomes; however, the number of reported COVID-19 positive pregnant women was small in relation to the total pregnancies in our cohort, and it is unlikely that viral infection would change our results.43 Although, we did not investigate variations across the different regions of Manitoba, the changes to maternal healthcare were implemented across the provincial healthcare system minimizing any differential variability within the data. However, some rural regions may have been substantially impacted during the pandemic compared to larger cities. Moreover, we did not include out of hospital births, maternal smoking and alcohol/substance use, and vaccination rates among pregnant women in Manitoba

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275603: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our cross-sectional study has limitations, including self-report of testing outcomes over a 14-day recall period (subject to recall bias) and limited sample size especially in subgroups of those with evidence of COVID-19. For those with prior COVID, we did not capture information on timing of prior infections, which underestimates the degree of hybrid protection, though a substantial proportion of NYC adults were infected during the recent BA.1 surge.9,16 Our case definition would likely capture some, but not all, of the estimated 20-30% of individuals whose SARS-CoV-2 infection may remain asymptomatic throughout their infection,30,31 as well as those who were symptomatic but were not aware of a close contact. Finally, our survey could not include those whose primary language was not English or Spanish. Strengths include the representative nature of the study, the study’s timing at the start of the BA.2 /BA.2.12.1 surge, and measurement of several important factors that are not currently available through routine surveillance, including outcomes among those who do not test positive with a provider, prevalence among individuals vulnerable to COVID-19, hybrid protection, and awareness/uptake of nirmatrelvir/ritonavir.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275610: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

gross

SciScore for 10.1101/2022.05.26.493529: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.26.493539: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275552: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Another limitation is the possibility that some relevant studies were not taken into account because they have been published in languages other than English (e.g., Chinese). We also did not have access to some other databases that may store some articles on COVID-19 and cognitive dysfunctions. And lastly, there could be some other studies on this theme in the literature that skipped our attention and analyses. However, a comprehensive search strategy that covers a broad range of evidence was implemented. This systematic review gathered evidence that could provide clarity on the association between brain fog and COVID-19 infection. The information acquired in this study may help re-evaluate the impact of the virus. Furthermore, the use of the data gleaned from this analysis may assist in earlier treatment, allowing physicians and clinicians to manage the neurological manifestation effectively. Additionally, this will aid in the development of various therapeutic strategies to support COVID-19 patients in recovering from impaired cognitive capacity. Finally, the analysis of such data could provide an insight into the challenges that this virus could cause people in their prime years, particularly those in the workforce.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275549: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

4.3 Limitations and future research opportunities: This study has several limitations, which pose opportunities for future research. This is an ecological study using aggregated data at the county level. It is subjected to ecological fallacy. Future studies can use individual level data or experimental studies to confirm the causal relations and the potential underlying mechanisms (Jiang et al., 2021). Second, the unit of analysis is the county due to the availability of COVID-19 mortality data and other confounding variable. Though county data are widely used in nationwide studies, future studies should use finer-grained data (i.e., census tract level data). Different scales of analyses may reveal different associations between neighborhood greenspace and health outcomes (Richardson et al., 2012). Third, our research investigated associations using data from 2020, but the situation has continued to evolve with the emergence of vaccines and COVID-19 variants (e.g., Delta and Omicron). Future studies should consider the new situations accordingly.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275508: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations: There are several limitations to this study, First, mortality during 2020 is almost certainly under-estimated. Deaths during 2020 were based on an early release file for the National Death Index (NDI), which according to the National Center for Health Statistics, accounted for only about 95% of all recorded US deaths in 2020 at the time of the NDI search (Ryff et al., 2022). Second, the MIDUS sampling frame excluded the institutionalized population, who suffered especially high mortality early during the early stages of the pandemic. Thus, mortality among the MIDUS cohort is likely to be lower than pandemic-related mortality for the population as a whole. Third, we have no information about the degree to which MIDUS participants complied with public health orders during the pandemic and whether it differed by personality. Nor do we have any information about self-destructive behaviors (e.g., alcohol and drug abuse) during the pandemic. Fourth, personality was measured in 1995-96, approximately 25 years prior to the pandemic. Finally, the MIDUS sample under-represents minorities, who suffered higher mortality during the pandemic. Future Analyses: It will be useful to replicate this analysis using the Health Retirement Survey (HRS), which samples Americans older than 50, once mortality data become available for 2020. HRS has a much larger sample than MIDUS and thus, will yield more statistical power for modeling excess mortality. HRS also has a more ethnically dive...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.22.22275422: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.22275421: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275498: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

There were two limitations that the health workers noted about the study. First, the study was designed as a pilot to the Kisumu County Department of Health, in which the usage of AgRDT was done alongside confirmatory PCR testing. Although participants were informed about the possibility of discrepant results, these did result in some confusion. It was found out that a major cause of the discrepancy was attributed to non-adherence by healthcare staff to the study procedures where sometimes one and the same nasal pharyngeal swab was used for both AgRDT and PCR testing. This could result in false-negativity, due to dilution of the sample (15). Secondly, the test-result communication channels were not always clear and changed during the project, involving 3 different partners in addition to the participating facilities. There were no formal DoH protocols on communication channels, and in some cases feedback was not relayed to patients in a timely manner causing frustration and anxiety (16).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.22275562: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275398: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Study limitations: While our study provides insight into the variability and natural history of long COVID, there are limitations that should be considered. While the U09.9 code provides a simple inclusion criterion, its application in health systems across the country is not uniform and may differ from one data partner to another. Also, since the use of the code began only recently, patients with long COVID that were diagnosed prior to the introduction of the code are not included, limiting our ability to compare the current clinical manifestations with those observed earlier in the pandemic before widespread vaccination and with different distributions of SARS-CoV2 strains and variants. However, in a pilot study in Denmark, coding with U09.9 was found to have a positive predictive value of 94% for long COVID.56 Our ability to capture clinical manifestations of long COVID is limited by the accessibility of clinical data in EHR systems. Of the 287 HPO terms we identified as being used in published cohort studies on long COVID,19 only 116 were identified in our data. The reasons for this presumably include unstructured data such as symptoms and radiological findings that are not well represented in the OMOP data that is the source of our data. Examples include Gaze-evoked nystagmus (HP:0000640), Pericardial effusion (HP:0001698), and Exercise intolerance (HP:0003546) that are typically diagnosed using specialist examinations or medical history that may not be easily coded in s...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Answering the question:

I don't understand the point of putting it in a spec. What does that gain you over using benchmark-ips the normal way?

Data Discovery/Status Report

Click Here for full report

Summary of datasets and Open Science materials associated with article: - 4 Dataset(s) - 1 Code(s)

1 Image Dataset(s) - 1 "X-Ray Computed Tomography"

2 Genetic Data Dataset(s) - 1 "High-Throughput Nucleotide Sequencing" - 1 "Sequence Analysis"

1 dataset Dataset(s) - 1 "dataset"

SciScore for 10.1101/2022.05.24.493347: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275454: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.25.493397: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275444: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The study has several limitations. First, this was a retrospective study and, therefore, has a potential for biases from incomplete clinician documentation. Second, follow-up information was not available after patients were discharged, which may have led to underestimated rates of hospitalization or mortality. Third, members of the team reviewing the ED revisits were not blinded, which may have introduced assessment bias. Fourth, since different variants of coronavirus have different clinical characteristics, important factors at the triage may be different during other pandemic waves. Lastly, the generalizability of the current study findings to other settings is limited by single-center design. Because of site-specific characteristics, it is possible that other ED sites with differences in availability of transportation and access to professional medical support may have different rates of outcomes than our own patient cohort. In summary, approximately 80% of patients with mild COVID-19 disease can be safely isolated at home in a facility. Ten percent of patients will experience progression of symptoms in the ensuing week that will require hospitalization for treatment—typically at 10 days of symptomatic illness. Clinicians should inform patients, especially those aged >□50 years, or those that are obese with BMI > 25, or those with underlying hypertension, or those presenting to the hospital > 3 days after symptom onset and presenting tachycardia (PR > 100/min) or high bl...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.22.22275323: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275460: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

A limitation of our study was that we assessed levels of immunity from serum only. There is clearly also a role for mucosal immunity in protecting against SARS-CoV-2 infection, especially in the case of infection-acquired immunity. For the last three session in our study we collected nasopharyngeal samples, which we plan to incorporate in future research. Our analysis is dependent on the suitability of neutralizing titers as a correlate of protection against symptomatic COVID-19, based on meta-analyses of vaccine studies (7, 8). This assumption is supported by an analysis of data from phase 3 trials of Moderna’s mRNA-1273 vaccine, which indicated that 68% of vaccine efficacy can be explained by neutralizing titers (31). This leaves up to 32% variation that may be explained by other effects such as cellular immunity or host factors. An additional limitation is that the evidence base for neutralizing titers as a correlate of protection is built on studies of infection with the Ancestral variant. However, antibody levels have been observed to be associated with reduced infection with other variants, most notably Delta (32). Although neutralizing titers have frequently been shown to be associated with protection against severe COVID-19 (7-9), there is a weaker evidence base for their use as a correlate of protection. A final, important limitation is that there is uncertainty in the statistical relationships utilized in this analysis. When considering the inferred protection from ...

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04750720</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Study of the Kinetics of COVID-19 Antibodies for 24 Months i…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04441684</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Seroprevalence of SARS-CoV-2 in Strasbourg University Hospit…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04644159</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Longitudinal Follow-up of a Population Cohort in a French Ci…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04325646</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Sero-epidemiological Study of the SARS-CoV-2 Virus Responsib…</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275445: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Strengths and limitations: Strengths of our study include the use of sequenced data allowing comparison of omicron vs delta during the same calendar period when the two variants had the largest overlap, combined with health care register data with no attrition. Further, equal access to SARS-CoV-2 testing at no cost for the individuals as well as a universal tax-funded health care system, improve generalizability of findings to other countries. A limitation of our study is that we could not include antigen or home tests as they were not registered. Polymerase chain reaction testing was however mandatory for everyone with a positive antigen test during our study period. Moreover, all parcitipants in our study had a PCR test in a period characterized by great uncertainty regarding the severity of the new SARS-CoV-2 omicron variant. It is possible that our population consisted of particularly health-conscious persons who were highly prone to get tested and who were more prone to seek medical care after knowing they had been ill. We believe our methodological approach ensuring comparison of persons who were tested in the same calendar week limits this potential bias arising due to anxiety. Further, we have previously reported that register-based studies comparing persons with positive test with persons with negative test likely will lead to an underestimation of post-covid outcomes’ prevalence, as persons testing themselves may represent a particularly health-conscious sample of t...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.22.22275183: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275458: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.493187: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275411: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04456153</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Atovaquone for Treatment of COVID-19</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275364: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.24.22275544: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our findings have limitations that warrant discussion. This EHR-based study identified symptoms, signs, and diagnoses that were significant enough to prompt health service use and be coded by clinicians as a reason for an encounter. Our approach may have missed some findings that may be stored in laboratory, procedural, radiological and other unstructured text data. The true burden of PASC may be underestimated from EHR data, with data from open health systems, with the potential for infrequent follow-up visits to academic centers of excellence for milder symptoms. For this reason, we limited our cohort to at least one visit in the prior three years, to identify active patients within the health system; similar strategies have been employed in other PEDSnet studies of COVID-19;39,40 the benefits of such approaches have been reported previously.41 However, this may underestimate the burden of PASC by excluding previously healthy children who did not have prior health encounters. Next, our test negative cohort may include individuals with SARS-CoV-2 infection who may have had testing conducted outside our health systems. These limitations may have biased results towards the null. Further, we did not identify specific race/ethnic groups as a risk factor for PASC despite the fact that SARS-CoV-2 disproportionally affects minority communities, which may reflect differences in care seeking behavior and access to care. Finally, children who tested positive may be more likely to seek...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275439: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitation: The number of persons enrolled in the two groups, symptomatic and asymptomatic, may not reflect the true prevalence in the general population as we evaluated the Ag RDT during a period of high COVID-19 prevalence.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

General Statements [optional]

We are grateful for the very kind, thoughtful, and detailed comments of the reviewers, which we have strived to fully integrate into the revised manuscript.

Of note are the concerns with the data from stages S21 and S22, which we acknowledge do appear to be qualitatively and quantitatively distinct from the other samples. While we are unable to completely disambiguate meaningful biological variation from technical or experimental noise using our data, we hope a few additional analyses and visualization tools we have included can provide greater confidence in the reliability of our findings.

Additionally, while attempting to evaluate Reviewer #2’s suggestions about examining the distribution of intergenic peaks along the genome, we discovered an error in our code that resulted in the improper assignment of peak categories. The error resulted in the improper assignment of intronic and exonic peaks as intergenic peaks. While the largest group of peaks in our dataset remains distal intergenic peaks (30.2%), and distal intergenic peaks remain a larger proportion of our intergenic peaks than proximal intergenic peaks, many of the peaks originally assigned to the intergenic categories have been reclassified as exonic or intronic peaks. We have updated our code and figures upon reanalysis of our data and have revised our findings and discussion accordingly.

Description of the planned revisions

Reviewer #3, Comment #3 of 11_

“In general, I thought that the bioinformatic methods (i.e., the code or the options used for each program) would have been helpful for my understanding in some cases. The authors say that these will be published on an accompanying GitHub repository, which should be fine if this is sufficient for journal policy.”_

We are still at work compiling the code for our analyses into a more reader-friendly form and setting up a GitHub repository to enable easy access to more detailed methods for interested readers. Some of the most important settings have been included in the Methods and Supplementary Methods sections, but we hope to include more thorough detailing of our pipelines in the GitHub repository. The raw data for portions of the RNA-Seq and all of the ATAC-Seq data have been uploaded to the Sequence Read Archive, and we are finalizing additional raw data submission. We are also in the process of determining what data to include in our Gene Expression Omnibus submission, which we hope to include all pertinent final data analysis files as well as any intermediate or accompanying datasets which would facilitate downstream analyses. The large size and number of our final analysis files has resulted in some challenges with data transfer and storage, which has delayed the upload and submission process.

We are also collating several of the data visualization scripts built for this manuscript into a Jupyter notebook. This tool will enable the visualization of ImpulseDE2 models and peak classifications for arbitrary genes and genome regions of a user’s choice, alongside additional functions which are discussed in this revision plan.

Description of the revisions that have already been incorporated in the transferred manuscript

We have addressed the following substantive concerns with the manuscript:

Reviewer #2, Comment #2 of 3:_

“Authors have repeatedly used S21 and S22 throughout the manuscript to support their claims with clustering etc. May authors shed some light on the differences in replicates for these timepoints. Furthermore, I could not find Fig 3J, perhaps author would like to point out Fig 3H.”_

Reviewer #3, Cross-comment #2 of 3:_

“Focus on stages S21/S22: This might indeed be somewhat problematic. The libraries from these two stages (particularly S21) seem to be very different from those from the other stages. In the PCA (Fig. 1C), S21 doesn't cluster well with anything, and the difference between the two replicates is massive compared to other stages. The accessibility pattern (Fig. 1D) also looks odd. The libraries also have the lowest scores for % of mapped reads (Fig. S2B), fragment size distribution (S2E), and Spearman correlation (S2I). All this could be biologically sound and be due to a major developmental transition at this point, but maybe it justifies revisiting the data and testing whether leaving out S21 (and/or S22) makes a big difference for the clustering analyses.”_

1. Reviewers #2 and #3 discussed concerns with the outlying nature of libraries S21 and S22. We had also previously held concerns about these samples and had performed some analyses to examine whether the global properties of our dataset are dramatically changed upon removing those samples. We did not observe dramatic changes to the structure of our data in the absence of the S21/S22 samples.

   • a. Samples S21 and S22 appear to be highly separated from the rest of our data using Principal Components Analysis. We had also previously believed that this suggested that these samples might be problematic. However, a colleague indicated to us that researchers in microbiome ecology had observed similar phenomena, often caused by strong single axes of variation (or “linear gradients”) in the datasets. In “Uncovering the Horseshoe Effect in Microbial Analyses” (mSystems, 2017) by Morton et al., the authors describe how a strong linear gradient can create a “horseshoe effect” or “Guttman effect”, where PCA results in the two ends of a linear gradient appearing to come together in ordinal space. The authors also describe a similar “arch effect” which strongly resembles the general shape of our PCA curve. We suggest that the strong apparent “outlier” appearance of S21 and S22 may be exaggerated or induced by the technical “arch effect” phenomenon, and may be caused by a strong single biological gradient – a developmental timecourse – which our data aimed to capture.
   • b. We also performed PCA on our dataset with the S21 and S22 time points removed prior to performing the analysis (see right panel, bottom). When we did so, we observed that the relative positions of the remaining libraries remains largely similar, with time points closer to the middle of development showing a positive loading in PC2, and time points closer to the beginning and end of development showing a negative loading. This suggests that the second major axis of variation in our dataset would remain a contrast between middle vs. terminal timepoints, even without the S21/S22 data, and that the relative positioning of the remaining data within PC-space is not entirely driven by S21/S22.
   • c. To further assess the degree of the S21/S22 samples’ outlying effects, we also performed ImpulseDE2 analysis to generate model fits without S21/S22 data. Doing so allowed us to determine to what degree the S21/S22 stages are necessary for driving the accessibility trajectory of individual peaks, and of the data more broadly. We performed IDE2 with either all data, or the S21/S22 data removed prior to input into IDE2. This generated two sets of model fits to the “cloud” of accessibility vs. time measurements: one that included the S21/S22 data, and one without. We evaluated, for each peak in our dataset, the time point at which the IDE2 model achieved maximum accessibility (the “IDE2 max fit”), and plotted both the “all” and “noS21S22” data as a histogram (see right panel, top graph). The presence of peaks that achieve predicted maximum accessibility in the S21/S22 stages in the “no S21/S22” data is a result of how we calculate “max fit”, which does not require that there is a known accessibility value at a given timepoint; only that the time point during which the model fit is maximum is closest to the timing of that developmental stage. Overall, we still observed early, middle, and late enrichment of IDE2 max fit even when the S21/S22 data are removed. We do see a rightward shift in the middle timepoint histogram in the direction of later stages, although this may be expected given the absence of concrete accessibility values at S21/S22 in the “no S21/S22” data. This indicates that our data globally retain the general trends of early, middle, and late enrichment of accessibility in the absence of the S21/S22 data. Moreover, this suggests that, even without the S21/S22 data, the remaining data from early and late stages result in a model fit that still predicts maximum accessibility at middle developmental stages for many peaks.
   • d. To further measure the influence of the S21/S22 data in IDE2 model fit, we also evaluated the degree of change in the global behavior of a peak when the S21/S22 stages were removed. This analysis aimed to assess whether removing S21/S22 data resulted in an IDE2 model with the same general trajectory as with all data, as opposed to the more stringent requirement of evaluating whether the exact developmental stage of the peak was changed. To perform this analysis, we grouped developmental stages into five quintiles, each representing three stages of development. We asked, for each peak in our dataset, whether that peak’s IDE2 max fit was “stable” when the S21/S22 data were removed; that is, if the quintile of the IDE2 max fit was altered when the S21/S22 data were removed (i.e. if a peak moved more than 3 developmental stages away from its original position), a peak was considered “unstable”. We observed that over 80% of peaks in each quintile remained “stable” after removing the S21/S22 data, suggesting that the vast majority peaks show the same general trajectory of accessibility even without the S21/S22 data. Peaks within the middle time points appeared to be more unstable than peaks at the terminal timepoints, which could be expected given that the S21/S22 timepoints constituted the middle-most timepoints in our dataset.

We acknowledge that the S21/S22 timepoints still appear to be qualitatively different in other ways. Moreover, we acknowledge that some of the peaks in our dataset are “dependent” on the S21/S22 stages, given that their accessibility trajectory changes when these stages are removed. It is difficult to determine whether a change in accessibility trajectory for a given peak caused by the removal of S21/S22 data is indicative of technical differences in sample preparation, such as batch effects; biological variation, such as a potentially unknown mutant or sick embryo; or due to genuine wildtype biological processes that occur at the S21/S22 stages.

These caveats acknowledged, a comparative analysis of the data in the absence of the S21/S22 stages suggests that much of the global picture of development remains the same. In the interest of providing the data we generated as a resource, we decided to include the S21/S22 data in the final manuscript we have prepared for submission.

We have included an additional supplementary figure (Supp. Fig. 2.2) highlighting these further analyses, which we hope future readers will consider when performing their own analyses with these timepoints, as well as a summary of the ways we evaluated this potential concern in the Supplementary Methods. To facilitate future users of this dataset, we will include the model parameters calculated from IDE2 using both the full dataset and the data with S21/S22 removed in the GEO accession data, as well as a Jupyter notebook (ParhyaleATACExplorer.ipynb) that allows users to plot the raw accessibility data and IDE2 model fits for individual peaks of interest (C, example on right panel), so that downstream experiments can consider the potential differences with the S21/S22 samples.

Reviewer #2, Comment #3:_

“The majority of ATAC-seq peaks in the distal intergenic regions is a very surprising result. Authors defend this result by suggesting that this organism has big genome. May author perform a short analysis that shows that these peaks are indeed represent nearby genes or may point towards 3D genome organisation. For example, I see that this genome might have regions in the genomes that are densely organised in gene clusters, in those cases does the pattern remains same i.e he majority of the genes are very distant from each other and hence use vital regulatory elements?”_

Reviewer #3, Cross-comment #3 of 3:_

Peaks in distal intergenic regions: I agree that this could be elaborated on. It might also be that >10 kb is not actually that distal for Parhyale. I would suggest to split the "distal peaks" further (e.g., in 10 kb or 2-log steps, or whatever makes most sense) and try to understand if >10 kb is mostly <20 kb, or if most of them are hundreds of kb from the nearest gene?_

1. Reviewers #2 and #3 expressed interest in understanding the absolute distribution of distal intergenic peak distances from nearby genes in our dataset. In generating the analyses to address this question, we stumbled upon an error in our code that reveals that the true number of intergenic peaks is much lower than we had originally reported. We discuss the nature of the error below. Moreover, we address the previous question using the new data, which overall still indicates that distal intergenic peaks remain a large portion of the Parhyale genome.
   • a. To address Reviewer #2’s comments with respect to the presence of potential clusters of intergenic regions, we built a Python tool (included in ParhyaleATACExplorer.ipynb) enabling the visualization of different cis-regulatory element categories along a genomic coordinate. Upon plotting our data with this tool, we observed problems with the categorization of the peaks – namely, that intronic and exonic peaks were erroneously classified as intergenic peaks (see right panel, top). We analyzed our script for classifying annotations more carefully and realized that we had erroneously used “bedtools closest” instead of “bedtools intersect” to try to identify all peaks overlapping with gene annotations in our genome. We corrected this error and observed the expected distribution and categories of peaks in our data (right panel, bottom).
   • b. The revised peak categories have been added to the updated manuscript in Fig. 3H and Fig. 5C. The categories of peaks we observed differ substantially from our previous results, in that we observe a much higher representation of exonic and intronic peaks in our dataset, with intronic peaks now representing 28.2% of all peaks (increased from <1%), and distal intergenic peaks representing 30.2% (decreased from 51.2%). While distal intergenic peaks remain the largest category over time, the proportion is relatively equal to the fraction of intronic peaks. Intergenic peaks (distal and proximal combined) now make up only a slightly larger fraction of peaks (37.2%) than gene body peaks (exon, intron; total 34.4%). This updated result is a significant departure from our previous report, and we have updated the text of the manuscript to correct this mistake.-
   • c. While intergenic and distal intergenic peaks constitute a much smaller portion of our data, we still wanted to address Reviewer #2 and #3’s questions about the distribution of distances between intergenic peaks and nearby genes. We generated a plot to illustrate the number of intergenic peaks at variable distances to the nearest gene (B, right panel). As illustrated in the plot, there are a very large number of distal intergenic peaks, including many peaks >100kb away from the nearest gene. The average distance of intergenic peaks from the nearest gene was 73,351bp. We neglected to mention in the original manuscript that one of the rationales for choosing a 10kb cutoff as “distal intergenic” was that peaks beyond this distance would be considerably more difficult to isolate as single fragments combined with a proximal promoter using PCR, agnostic of their orientation with respect to the promoter element. Such peaks could not have been easily identified using previous transgenic approaches, and are thus distinguished from “proximal” peaks by their necessary identification using techniques such as ATAC-Seq. We have updated the text to reflect this distinction.
   • d. Given that both intergenic and gene body peaks appeared to comprise large fractions of our revised data, we also examined the relative enrichment of intergenic and gene body peaks with respect to time (after normalizing for the fraction of “unknown” peaks, as suggested by Reviewer #3). We observed that the proportion of peaks belonging to intergenic and promoter regions declined slightly as development progressed, while the proportion of gene body peaks increased (E, below). There appeared to be slightly more intergenic peaks than gene body peaks at all developmental time points, and the ratio of intergenic peaks to gene body peaks declined very slightly over time (F, below). These data indicate that intergenic and gene body peaks have different enrichment trajectories over time. As development progresses, gene body peaks are increasingly enriched, and may have a greater impact on gene regulation. We have added these additional observations to the text and to a new Supplementary Figure 2.3.

We have also addressed the following textual and conceptual concerns with the manuscript:

Reviewer #3, Comment #1 of 11_

I felt that the first paragraph of the introduction is not necessary._

1. We believe the introductory paragraph helps frame the paper in the context of the broader scope of advances in technologies for emerging research organisms – currently, it has become straightforward to both generate a genome sequence and to identify and manipulate coding genes of interest across diverse taxa, but the identification of gene regulatory mechanisms remains more difficult. We have edited the introduction to better reflect this perspective and to link the first paragraph to the rest of the paper.

Reviewer #2, Comment #1 of 3_

“In Introductory paragraph 2, sentence one, authors suggest that gene regulation plays more important role in evolutionary process than genes. Although a significant amount of research has been dedicated to gene regulation based evolution still this field is in nascent form. For example evidence of inheritance of the gene regulation pattern across generation is scarce and requires more evidence. I suggest authors to modulate the claim that still gene based evolution is the main paradigm instead otherwise.”_

Reviewer #3, Cross-comment #1 of 3_

Evolution via gene regulation vs. coding sequence: While (to my understanding) it is largely accepted in the field that changes to the CDS will often have more deleterious effects than changes to the expression of a gene, I agree that this could be elaborated on a bit.

1. As requested by Reviewers #2 and #3, we have clarified the language surrounding the debate between gene functional and gene regulatory evolution to indicate that both mechanisms appear to be important for evolutionary processes, with the importance of the latter more recently revealed.

Reviewer #3, Comment #2 of 11_

Use of Genrich: I presume this was run on both duplicates simultaneously? This is not clear from the methods section. It might have implications for downstream analyses (e.g., differential accessibility between time points) because running on both sequencing library replicates simultaneously leads to a single "replicate" of peaks per time point, while running it individually leads to two. However, I have never tested if this actually does make a difference. Maybe the authors have and can comment on this?

1. In response to Reviewer #3’s inquiry about Genrich, we have added additional clarifying information into the Methods section. “Genrich analysis was run on both duplicate libraries simultaneously; Genrich performs peak calling on each peak individually, and then merges the p-values of the replicates using Fisher’s method to generate a q-value, obviating the need to calculate an Irreproducible Discovery Rate (IDR).” We did not test running Genrich on individual libraries, opting for the more conservative approach of using the combined q-value as a filtering score for peak quality. For further information, the reviewer can see the Genrich Github repository section here: < [https://github.com/jsh58/Genrich#multiple-replicates]

Reviewer #3, Comment #4 of 11_

The section on the IDE2 models (the paragraph at the end of page 4/beginning of page 5) was unclear to me but appears sound. (The only instance where I didn't quite understand what the program actually does.) Maybe this can be explained a bit easier?_

1. As requested by Reviewer #3, we have attempted to explain the methods and logic of using ImpulseDE2 a bit more clearly:

“To identify regions of dynamically accessible chromatin, we used the ImpulseDE2 (IDE2) pipeline (Fischer et al., 2018). IDE2 differs from other software for differential expression analysis in that it allows the investigation of trajectories of dynamic expression over large numbers of timepoints. It does so by modeling a gene expression trajectory as an “impulse” function that is the product of two sigmoid functions (Chechik and Koller, 2009; Yosef and Regev, 2011). This approach enables the modeling of a trajectory of gene expression in three parts: an initial value, a peak value, and a steady state value, thus summarizing an expression trajectory using a fixed number of parameters. With the ability to capture the differences between early, middle, and late expression values for each gene in a dataset, IDE2 also enables the detection of transient changes in gene expression or accessibility during a time course. Identifying differential expression over large numbers of timepoints is difficult for more categorical differential expression software such as edgeR and DESeq2, which generally use pairwise comparisons between timepoints to assess change over time (Love et al., 2014; Robinson et al., 2010).”

Reviewer #2, Comment #2 of 3_

2-2) Authors have repeatedly used S21 and S22 throughout the manuscript to support their claims with clustering etc. May authors shed some light on the differences in replicates for these timepoints. Furthermore, I could not find Fig 3J, perhaps author would like to point out Fig 3H.

Reviewer #3, Comment #5 of 11_

On page 7, Fig.3J needs changing to 3H. This figure should, in my opinion, also contain the absolute number of peaks for each time point to set the individual proportions into context.

1. As requested by Reviewer #3, we have added a bar charts representing the number of peaks found at each time point (Fig. 3H) and the number of peaks found in each cluster (Fig. 5C) to the peak type proportion plots. We have also fixed references to Fig. 3J to instead refer to Fig. 3H – we apologize for the confusion.

Reviewer #3, Comment #6 of 11_

Last paragraph of the "Improving the Parhyale genome annotation" section: I think this needs to focus on those regions of the genome for which the location is known - after all, the "unknown" regions" could all be "distal transgenic", which would significantly change the relative proportions._

1. We have revised our analysis of this topic with our updated peak type proportions, as described above in point 2d above under “substantive concerns”.

Reviewer #3, Comment #7 of 11_

“On page 9, t-SNE is mentioned but doesn't seem to be cited.”

1. As requested by Reviewer #3, we have added citations for the t-SNE method, as well as scikit-learn, the software we used for t-SNE visualization.

Reviewer #3, Comment #8 of 11_

“The third paragraph on page 9 ("We evaluated the differences...") should mention the fact that clusters 1 and 2 are the only ones with significant proportions of exonic and intronic peaks. In the accompanying figure (5C), the total number of peaks would again be helpful.”_

1. After identifying the error in our peak category classification pipeline, this observation was no longer true. However, upon examining the new distributions by cluster, we observed that in Clusters 3–7, for which we observed GO enrichment for developmental processes, there appeared to be slightly higher enrichment of intronic regulatory elements than distal intergenic regulatory elements. These results resemble the observation from recent work showing that tissue-specific enhancers are enriched in intronic regions in various human cell types (e.g. Borsari et al. 2021, Genome Research). We have noted this new observation in the text.

Reviewer #3, Comment #9 of 11_

In figure 5D, I can't quite make out at which stage the dip in the peak of Cluster 8 occurs. This is quite an unusual pattern of accessibility change, and I can't help but wonder if it has something to do with the quality of one of the libraries? Also, the fact that half of the peaks fall into unmapped regions of the genome is unusual, and I feel this deserves more discussion._

1. In Figure 5D, Reviewer #3 asks about a dip in accessibility for Cluster 8 peaks. The dip in accessibility was actually observed for Cluster 9 peaks and is marked by the asterisk in that panel. We have updated the figure legend to clarify the significance of the asterisk and have referred readers to examine Supp. Fig. 5.1B, where the IDE2 model fits more clearly show a collective dip in accessibility for Cluster 9 peaks. Upon examining the size distribution of the clusters, we have also noticed that Cluster 8 is the smallest cluster. We have noted the small cluster size and high “unknown” peak enrichment for Cluster 8 in the text.

Reviewer #3, Comment #10 of 11_

“On page 10, the abbreviation PFM appears, but it is only explained in the legend of Fig.4. This should appear in the text.”_

1. Reviewer #3 mentions that on page 10, we use the abbreviation for position frequency matrices (PFMs) without previous reference. We first introduce the abbreviation on page 8, but given the repeated use of “PFM” on page 10, we have added an additional explanation of the abbreviation on page 10, for ease of reading.

Reviewer #3, Comment #11 of 11_

“The section on "Concordant and discordant expression and accessibility" is the one I disagree most with. The authors seem to suggest that a repressive cis-regulatory module should become less accessible when the gene is activated. However, they leave trans-acting factors completely out of their conceptualisation here. It is in general likely the availability of transcription factors that leads to repression, while the "silencer" can be well accessible in all cells. Moreover, it has become clear in recent years that CRMs are not just repressors or enhancers per se but can act as either depending on the availability of transcription factors. I think these facts could partially explain the weak correlation and should be discussed.”_

1. We appreciate the comments from Reviewer #3, which alerted us to the more recent literature around the bifunctional potential of regulatory elements. We have revised our claims to clarify that concordance and discordance analysis cannot be used to directly assign “enhancer” or “silencer” identity to given regulatory elements. Instead, we suggest that evaluating concordance and discordance can be useful for downstream users of our data, such as those aiming to build reporter constructs for a given gene of interest. To facilitate such tool development, we have built additional functions into a Jupyter notebook to enable the visualization of accessibility, gene expression, fold change of accessibility and gene expression, significance of fold change, and concordance/discordance assignment for arbitrary peak-gene pairs. An example of this visualization is shown on the following page. Panel A shows the region around the Engrailed-1 and Engrailed-2 loci in Parhyale (text labels within the plot region were added manually in Illustrator). Panel B shows visualization of the En1 promoter peak alongside En1 expression. Significant log fold changes (DESeq2 padj < 0.05) are marked by asterisks in the bar plots, and concordance/discordance assignment at each time point is indicated by the color of the comparison text (red = concordant, blue = discordant). Panels C and D show accessibility and expression visualization for a single peak (En1 peak5) compared to two nearby genes (En1 and En2). We hope to include sufficient documentation in our GitHub repository such that using these tools is accessible for most researchers, even with limited programming knowledge.

Description of analyses that authors prefer not to carry out

We were unable to easily visualize the distribution of regulatory elements across the whole genome as suggested by Reviewer #2. One challenge of working with the Parhyale genome is the lack of complete chromosomes. The genome is distributed across ~290,000 contigs of variable size. We were unable to find any software that could be easily and quickly set up to visualize our data, although we will provide in a Jupyter notebook the tools for local visualization of peak types that we developed.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

In this study, Sun et al. use RNAseq and ATAC-seq in 15 stages of embryonic development of the amphipod crustacean Parhyale hawaiensis to analyse gene regulation genome-wide. They assess the data in multiple ways to provide a more complete genome annotation, understand temporal changes in gene regulation, and identify different classes of cis-regulatory elements including associated GO terms and putative transcription factor binding site enrichment. The authors have made a great effort to account for potential biases in their datasets (one impressive example is the comparison of multiple transcriptome assemblies and the following quality assessment) and I enjoyed reading this manuscript for its great explanations of method usage (i.e., what each bioinformatic package does, why it was used etc.) and the overall style.

I want to make a few suggestions that would make the study - in my opinion - even better:

• I felt that the first paragraph of the introduction is not necessary.
• Use of Genrich: I presume this was run on both duplicates simultaneously? This is not clear from the methods section. It might have implications for downstream analyses (e.g., differential accessibility between time points) because running on both sequencing library replicates simultaneously leads to a single "replicate" of peaks per time point, while running it individually leads to two. However, I have never tested if this actually does make a difference. Maybe the authors have and can comment on this?
• In general, I thought that the bioinformatic methods (i.e., the code or the options used for each program) would have been helpful for my understanding in some cases. The authors say that these will be published on an accompanying GitHub repository, which should be fine if this is sufficient for journal policy.
• The section on the IDE2 models (the paragraph at the end of page 4/beginning of page 5) was unclear to me but appears sound. (The only instance where I didn't quite understand what the program actually does.) Maybe this can be explained a bit easier?
• On page 7, Fig.3J needs changing to 3H. This figure should, in my opinion, also contain the absolute number of peaks for each time point to set the individual proportions into context.
• Last paragraph of the "Improving the Parhyale genome annotation" section: I think this needs to focus on those regions of the genome for which the location is known - after all, the "unknown" regions" could all be "distal transgenic", which would significantly change the relative proportions.
• On page 9, t-SNE is mentioned but doesn't seem to be cited.
• The third paragraph on page 9 ("We evaluated the differences...") should mention the fact that clusters 1 and 2 are the only ones with significant proportions of exonic and intronic peaks. In the accompanying figure (5C), the total number of peaks would again be helpful.
• In figure 5D, I can't quite make out at which stage the dip in the peak of Cluster 8 occurs. This is quite an unusual pattern of accessibility change, and I can't help but wonder if it has something to do with the quality of one of the libraries? Also, the fact that half of the peaks fall into unmapped regions of the genome is unusual, and I feel this deserves more discussion.
• On page 10, the abbreviation PFM appears, but it is only explained in the legend of Fig.4. This should appear in the text.
• The section on "Concordant and discordant expression and accessibility" is the one I disagree most with. The authors seem to suggest that a repressive cis-regulatory module should become less accessible when the gene is activated. However, they leave trans-acting factors completely out of their conceptualisation here. It is in general likely the availability of transcription factors that leads to repression, while the "silencer" can be well accessible in all cells. Moreover, it has become clear in recent years that CRMs are not just repressors or enhancers per se but can act as either depending on the availability of transcription factors. I think these facts could partially explain the weak correlation and should be discussed.

Significance

This manuscript will greatly advance research in the emerging model organism Parhyale through a more complete genome annotation and vast amounts of gene expression and chromatin accessibility data (and accompanying analyses) at various stages of development. However, the impact goes far beyond the Parhyale community, and I believe this paper can be seen as a blueprint for similar studies in other organisms. The excellent documentation and comparison of their bioinformatic methods makes their re-use straightforward and much of the authors' pipeline can be used for a "standard" ATAC-seq data analysis - I am likely to use many of their methods myself. Therefore, I think the audience can range from the "classic" evo-devo community to developmental biologists, scientists interested in gene regulation in general, and bioinformaticians.

My own expertise is in gene regulation through transcriptional control, and I use different seq approaches (ATAC, CUT&RUN, RNAseq) to study this process.

Referees cross-commenting

Thank you to my colleagues for their comments. Since Reviewer 1 was happy with the manuscript as it is, I'll only add my views to the points raised by Reviewer 2: - Evolution via gene regulation vs. coding sequence: While (to my understanding) it is largely accepted in the field that changes to the CDS will often have more deleterious effects than changes to the expression of a gene, I agree that this could be elaborated on a bit. - Focus on stages S21/S22: This might indeed be somewhat problematic. The libraries from these two stages (particularly S21) seem to be very different from those from the other stages. In the PCA (Fig. 1C), S21 doesn't cluster well with anything, and the difference between the two replicates is massive compared to other stages. The accessibility pattern (Fig. 1D) also looks odd. The libraries also have the lowest scores for % of mapped reads (Fig. S2B), fragment size distribution (S2E), and Spearman correlation (S2I). All this could be biologically sound and be due to a major developmental transition at this point, but maybe it justifies revisiting the data and testing whether leaving out S21 (and/or S22) makes a big difference for the clustering analyses. - Peaks in distal intergenic regions: I agree that this could be elaborated on. It might also be that >10 kb is not actually that distal for Parhyale. I would suggest to split the "distal peaks" further (e.g., in 10 kb or 2-log steps, or whatever makes most sense) and try to understand if >10 kb is mostly <20 kb, or if most of them are hundreds of kb from the nearest gene?

1. Heinrich von Kleist, “Ü ber die allm ä hliche Verfertigung der Gedanken beim Reden,” in Sämtliche Werke und Briefe. Zweiter Band, ed. Helmut Sembdner (M ü nchen: dtv, 1805/2001), 319 – 324, at 319.
2. Ibid., 320.

in 1805 Heinrich von Kleist noted that one can use conversation with another person, even when that person is silent, to come up with solutions or ideas they may not have done on their own.

This phenomena is borne out in modern practices like the so-called "rubber duck debugging", where a programmer can talk to any imagined listener, often framed as a rubber duck sitting on their desk, and talk through the problem in their code. Invariably, talking through all the steps of the problem will often result in the person realizing what the problem is and allow them to fix it.

This method of verbal "conversation" obviously was a tool which indigenous oral cultures frequently used despite the fact that they didn't have literacy as a tool to fall back on.

SciScore for 10.1101/2022.05.24.493348: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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SciScore for 10.1101/2022.05.22.492976: (What is this?)

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Table 1: Rigor

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No key resources detected.

Results from OddPub: Thank you for sharing your code.

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About SciScore

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SciScore for 10.1101/2022.05.23.493138: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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About SciScore

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SciScore for 10.1101/2022.05.18.22275112: (What is this?)

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This study has several limitations. First, due to the limited population size of Switzerland and the low incidence of glomerulonephritis, we cannot exclude a small effect of vaccinations, in particularly for MCD, as discussed above. Second, due to the decentralized health care system in Switzerland, patients were cared for by a variety of hospital-based and private practice nephrologists. Patient evaluation and care were thus not standardized and the study relied on patient- and physician-reported data. Third, due to the retrospective design of the study, patient-reported symptoms were subject to potential recall bias and onset of symptom dates were not precise in some patient questionnaires. Therefore, we chose biopsy proven glomerulonephritis as the primary outcome. Fourth, we were not able to include all patients with biopsy-proven glomerulonephritis in the second study and cannot exclude selection bias. However, the major reason for non-inclusion of patients was non-participation of their treating nephrology divisions or practices due to time constraints, which should not introduce bias. Finally, our results are limited to new-onset glomerulonephritis and cannot answer the question, whether SARS-CoV-2 vaccination could trigger relapses in patients with previously diagnosed glomerulonephritis, because relapses are usually diagnosed clinically without repeat biopsy. In conclusion, combining two complementary approaches, we did not find an association between mRNA-based vacc...

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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As conventional RT-PCR holds limitations to detect the negative strand in excess of positive strand copies, we employed a recently published strategy by Wiehler and Proud [32] to analyze the negative strand level. We observed that 2-DG significantly reduced the genomic (+)ssRNA as well as the template (-)ssRNA, a likely cause for the measured significant reduction in detectable extracellular viral RNA (Figure 4A&B). These findings point at a 2-DG-mediated impairment in viral RNA replication and amount of released virus. In line with this, titration of the released virus on HeLa Ohio cells showed a reduction in viral load (Figure 4C). To be noted, HeLa Ohio cells used in this experimental setup, due to their cancerous origin, have a high glucose demand and are especially sensitive to glucose starvation and 2-DG treatment. Therefore, low amounts of 2-DG were used, and the cells were treated only once after the start of the RV infection. This could explain the relatively small difference in viral load (Figure 4C) in contrast to the significant difference in released extracellular viral RNA (Figure 4B). In our subsequent analysis, we found that 2-DG exerted a protective effect by significantly reducing virus-induced cell death in HeLa Ohio cells (Figure 4D). In contrast, RV infection does not cause cell lysis in cultures of healthy bronchial epithelial cells [39]. Interestingly, the same study reported increased viral replication and cell lysis after RV infection in asthmatic bro...

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how to sort integers by creation time in objets

Everything can be treated as a database! We shouldn't have to worry about "replicating" the data - backups are good, but storing temporary sources of truth in some places with more permanent sources of truth in others is a little silly... what gives them that privilege and why is all the data constrained to the backend?

SciScore for 10.1101/2022.05.21.492554: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04324606</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">A Study of a Candidate COVID-19 Vaccine (COV001)</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04400838</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Investigating a Vaccine Against COVID-19</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.492922: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.493121: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.492903: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.492928: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

We now briefly mention several caveats pertaining to our study. At the current stage, we have intentionally used a relatively simple model that focuses on the magnitude of the antibody response following WU- and OM-vaccination. This is because at present, data on the dynamics following immunization and boosting is largely limited to titers of antibodies (6, 8, 37–40), serum biomarkers (8, 37, 38), and the virus inoculum (41, 42). We have much more limited data on the dynamics of different populations of cells responsible for the generation of humoral immune responses in the lymph nodes (39, 43). These would include different populations of dendritic cells, follicular CD4 T cells, as well as different populations of B cells and plasma cells (33, 34, 44–50). Further complexities specific to CoV-2 include the spatial aspect of infections of the respiratory tract (51–54) as well as the dynamics of production and distribution of antigen by mRNA based vaccines (55) as well as infections. As more data becomes available, it will be possible to construct more nuanced and refined models of the dynamics of humoral immunity as well as affinity maturation (56–62). Other directions that could be taken include modeling how protection is lost as the antibody titers elicited by the different immunizations wane. Gagne et al. showed that shortly after vaccination #3, both vaccines elicited similar levels of protection following virus challenge, and it will be important to know if and how this p...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.22274939: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, the inherent limitations of telemedicine restrict its utility, especially for health conditions that require physical assessments and laboratory tests. We found that telemedicine use and satisfaction are influenced by a number of factors including: safety during the pandemic, privacy, affordability, convenience and accessibility, and availability of more avenues of communication. Safety was a major concern that prompted participants to use telemedicine. Telemedicine enables patients to avoid situations that would expose them to SARS-CoV-2, the causative agent of COVID-19, such as traveling and staying for long periods in high-risk environments. These safety concerns, together with lockdown restrictions, resulted to significant declines in hospital admissions for non-urgent procedures (2). Innovative solutions through telemedicine have been introduced including video visits (25). Participants also mentioned that telemedicine assisted in maintaining privacy. The benefits of anonymity are especially important with regards to sensitive and potentially stigmatizing health issues such as mental or sexual health conditions (29). Because telemedicine removes the need to travel, participants also viewed it as more affordable and convenient. This was noted by participants as an enabling factor to use telemedicine, especially since a third of our participants are full-time employees, while a quarter are students. Traveling for healthcare purposes could mean missing work or scho...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.17.22275005: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The present study contains some limitations. First, the study relies on retrospective self-reported data by proxy, which i) may not directly reflect what a child had experienced and ii) underlies a risk of recall bias i.e. not recalling some not life-threatening symptoms several months later. However, the extensive public debate about SARS-CoV-2 vaccinations including their potential side effects, the off-label use investigated, and the study results showing dosage-dependent associations between vaccination and local reactions in ≥24 to <60 months old children suggest that symptoms were adequately reported. The retrospective design did not allow for a structured real-time documentation of symptoms nor an estimation of causality and severity to infer the frequency of vaccine-related serious adverse reactions, but such data should be expected from the ongoing prospective manufacturer-sponsored BNT162b2 studies (5). As vaccinations themselves were not directly part of the study, the study relied on vaccination information as reported by the respondents. However, a potential risk of manipulation by individual respondents was minimized by dispensing study participation codes only via vaccinating centers/initiatives, and the availability of BNT162b2 lot numbers. Moreover, potential duplicate entries were identified by overlap in demographic data and were removed. The reported post-vaccination symptoms could potentially be unrelated to the vaccine. To mitigate this problem, a non-BN...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.20.22275319: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.20.22275396: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.20.22275407: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.23.22275442: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The study has certain strengths and multiple limitations. Strengths include a large sample of well-defined post-COVID patient group, a socio-demograpically matched control group and the use of an innovative and sensitive tablet-based assessment tool with automated scoring. The most critical limitation is that our study cannot inform about whether the identified deficits pre-existed prior to infection. This is true even though we excluded patients with known relevant neurological disorders. Our study is furthermore prone to selection bias. As we tested a clinically referred sample of post-COVID patients, findings might not generalize to all post-COVID patients, but only to those who have symptoms severe enough to visit a specialized clinic. The OCS-Plus testing identified subtle long-term cognitive deficits persisting for more than 3 months in patients with post-COVID syndrome. A relevant next step will be obtain longitudinal data on memory, attention and executive functions from the presented cohort, in order to differentiate between deficits that might improve and those that might become chronic. This will provide specific information on targets for interventions, such as cognitive training. As many patients affected by post-COVID syndrome are living in medically underserved, rural areas, the mobile, cost-effective and highly scalable OCS-Plus assessment could be used for testing patients outside specialized facilities.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.21.492920: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.19.22274968: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study had certain limitations. Although it was cross-sectional, our data provide an important synthesis of symptomatology within a diverse population of people seeking community testing. Second, symptoms and timing of onset were self-reported which, may introduce bias; however, symptom data were collected prior to persons knowing their COVID-19 test result and thus would not be expected to result in differential bias between those who were COVID-19 positive and negative. Additionally, we did not characterize the severity of COVID-19 symptoms across waves, and even a single very severe symptom can cause substantially greater morbidity in an individual than multiple mild symptoms. Finally, testing was undertaken with rapid antigen tests, and thus may have resulted in misclassification of individuals during the earliest phases of their illness. Nonetheless, strict quality control measures were implemented to ensure accuracy of results and the use of rapid antigen tests. In summary, the clinical presentation of symptomatic persons changed over time during several COVID surges characterized by increasing population immunity and different SARS CoV-2 variants. During Omicron, when population immunity was higher, persons with symptoms often did not see improvement after 5 days and antigen tests frequently remained positive. These findings highlight the importance of work assurances to protect workers and requirements for rapid antigen testing to shorten isolation to protect the w...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.05.22.22275417: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Strengths and weaknesses: The key strengths of this study are the scale, level of detail and completeness of the underlying primary care EHR data and the linkage to multiple COVID-19 relevant national databases within the OpenSAFELY-TPP platform. In addition, the concurrent national rollout of sotrovimab and molnupiravir under similar indications between December 16, 2021 and February 10, 2022 enables us to make direct head-to-head comparisons for their effectiveness. Several limitations of this study need to be considered. Since the Omicron BA.1 variant was the dominant variant during the treatment period (December 2021-February 2022) [13], it is likely that our results can mainly be applied to this variant [14,15]. Whether the beneficial effect of sotrovimab persists for other variant subtypes warrants further investigation [14-17]. For instance, the Omicron BA.2 sublineage was shown to exhibit marked resistance to sotrovimab in in vitro experiments [18,19], whereas an in vivo experiment found both molnupiravir and sotrovimab can restrict viral replication in the lungs of BA.2- infected hamsters [20]. In addition, the patients included in this study are assumed to be only those who met the eligibility criteria made by NHS England [3], thus limiting further generalisation of our findings to people not in a known high-risk group. The possibility of residual confounding or measurement error cannot be ruled out in this real-world observational study, in particular related to di...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.