On 2021-03-24 00:09:50, user Elle wrote:

Also, how is it that patients were not broken out into smokers/non-smokers? All of these symptoms I think would be exacerbated by a smoking habit.

On 2021-03-24 15:21:57, user evacguy wrote:

I am pleased to annouce that this paper was accepted by the Journal of Travel Medicine following peer review on 11/02/21. It is noted that none of the findings, results or conclusions from the first draft have changed. The authors thank the reviewers for their insightful comments and suggested changes which improved our paper. The peer reviewed paper can be freely downloaded using the following link: https://doi.org/10.1093/jtm...

On 2021-04-05 17:44:22, user Armchair Hydrogeologist wrote:

Graph labels are swapped in Figure 1. Should be PepcidAC + Aspirin is 50% not other way around.

On 2021-04-06 17:16:29, user Rick Clem wrote:

I was infected in December along with my whole family. Loss of smell and<br /> a little lethargy was all we experienced. I have wondered if our luck <br /> was attributed to low loading factor or other. So I wonder on the <br /> degree of anitbody presence I attained from the infection. I received <br /> my 1st Moderna shot three weeks ago. Hit me like a freight train after <br /> 10 hours. Extreme fatigue, some headache. My thought is now directed <br /> to skipping my 2nd shot. Reading in the current studies on the <br /> necessity of a second shot, I hope they consider intensity of the <br /> previous infection in their studies. It would help folks like me to <br /> make a more informed decision on whether or not to ignore Fauci and the <br /> CDC's generalisms on needing a second shot.

On 2021-04-12 13:32:42, user H Arnold wrote:

Fantastic paper! What makes me a bit wonder is the discordance to the publications by Yost et al 2019 and Wu et al. 2020. Both report the replacement of T cells in the tumor (different entities) from external sources upon successful ICI.

Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, McNamara KL, Granja JM, Sarin KY, Brown RA, Gupta RK, Curtis C, Bucktrout SL, Davis MM, Chang ALS, Chang HY. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019 Aug;25(8):1251-1259. doi: 10.1038/s41591-019-0522-3. Epub 2019 Jul 29. PMID: 31359002; PMCID: PMC6689255.

Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O'Gorman WE, Au-Yeung A, Takahashi C, Goldstein LD, Poon C, Keerthivasan S, de Almeida Nagata DE, Du X, Lee HM, Banta KL, Mariathasan S, Das Thakur M, Huseni MA, Ballinger M, Estay I, Caplazi P, Modrusan Z, Delamarre L, Mellman I, Bourgon R, Grogan JL. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020 Mar;579(7798):274-278. doi: 10.1038/s41586-020-2056-8. Epub 2020 Feb 26. PMID: 32103181.

On 2021-04-12 15:56:55, user Philip Machanick wrote:

I am also curious how in a double-blind trial it comes about that someone in the control is given the test drug.

On 2021-04-26 03:17:28, user YingYing Irene Wang wrote:

The Journal of Deaf Studies and Deaf Education - Accepted for publication, DOI: http://doi.org/10.1093/deaf... --- online publication is under process. It will be online shortly. Thanks.

On 2021-04-27 09:15:58, user Ramy Ghazy wrote:

This manuscript describe the geospatial distribution of under-five mortality in Alexandria Egypt, moreover, we identified the main determinant of under-five mortality. We hope to help the health authority and stakeholders to decrease future increase in U5M.

On 2021-04-29 18:57:59, user Kirsty Short wrote:

Yes it has been accepted at CID https://pubmed.ncbi.nlm.nih...

On 2021-04-30 14:31:06, user Gustavo Bellini wrote:

congratulations on the study! it would be interesting if the dose of cholecalciferol and calcifediol used was reported. patients supplemented with Colecalciferol may have had less protection because they were supplementing with low doses, which were not sufficient to raise the levels of 25OHD to the ideal range, so that vitamin D performs its immunomodulatory functions at maximum level. it would also be very interesting if 25OHD levels were reported in the supplemented groups and in a sample from the control group.

it is also important to note that a daily dose of around 5,000 IU (person weighing> 50 kg) of cholecalciferol will cause the 25OHD levels to gradually increase and stabilize at around 50ng / ml only after 4 months. on the other hand, an attack dose of 600,000 IU of cholecalciferol in people with low levels causes the 25OHD levels to rise in 3 days to the optimum range. the level starts to drop after 15 days, and in order to stay in the ideal range, a daily (5,000 IU) or weekly (35,000 IU) supplementation with realistic doses should be started. if supplementation is not done continuously, the 25OHD levels fall back to around 20ng / ml in a 2-month interval.

• Daily oral dosing of vitamin D3 using 5000 TO 50,000 international units a day in long-term hospitalized patients: Insights from a seven year experience<br /> https://doi.org/10.1016/j.j...

• Effect of a single oral dose of 600,000 IU of cholecalciferol on serum calciotropic hormones in young subjects with vitamin D deficiency: a prospective intervention study<br /> https://doi.org/10.1210/jc....

On 2021-05-11 13:08:35, user Tomas Hull wrote:

There was no placebo group... <br /> If the same study was among the unvaccinated frontline health care workers, dealing with SARS-CoV2 patients, wouldn't most of them have at least detectable IgG and IgM titers??? <br /> Why not test the same group of people again 2-3 months later and see what the antibody titers are, if detectable at all...

On 2020-04-22 23:37:10, user Glenn Korbel wrote:

In the absence of tests for antibodies, which they don't have they are simply guessing/Yhere is NO way to predict deaths without knowing how many people have already been infected.<br /> None.

On 2020-07-16 21:40:47, user Marm Kilpatrick wrote:

Very nice study.<br /> Did you measure viral loads in patients? If so, would it be possible to include those to see if they might be implicated in 4 cases of infection? Sample size and power would be low, but it would be useful to at least take a look.<br /> It also wasn't clear if some HCWs had to engage in riskier activities (e.g. intubation) and this might have led to infection. Thank you!

On 2020-04-23 05:57:15, user David Feist wrote:

It is always good to compare data within nations. But in fact preliminary, linear regression analysis, from a fellow maths major, now seems to indicate that the lockdowns had no statistically significant effect within the USA: https://www.spiked-online.c....

This Santa Clara study indicates why Sweden, Japan, South Korea and Australia have not had public health apocalypses, with no lockdowns; the mortality rate was miscalculated.

On 2020-05-01 12:52:41, user Scott Howell wrote:

Any chance that the data and code can be made available? Seems fitting given the topic and re-analysis.

On 2020-04-17 21:38:11, user Michael Stein wrote:

There could be a very large upward bias due to the participants in the study being people who responded to the Facebook ad. It stands to reason that people who suspected they might have been exposed to the virus would be more likely to respond to such an ad. The fact that randomization was used to select who got the ads and that corrections for demographics were made does not address this potentially serious source of bias. There is little doubt that many more people have been infected than the official numbers, but I find the factor of 50-85 rather hard to believe in a place like Santa Clara County that has not been overrun by cases.

On 2020-04-18 04:34:06, user Zev Waldman MD wrote:

I agree with prior commenters that people who suspected that had or were exposed to Covid would be more likely to seek antibody testing. I see that participants were asked about prior symptoms, but it would also have been nice to ask about prior possible exposure concerns, If both numbers are very low, that would provide some reassurance about this possible bias.

I really wanted to address another issue: the calculation of the infection fatality rate, i.e., estimated deaths/cases. It seems that a lot more thought went into trying to get an accurate case count than an accurate death count. They seem to take it as a given that 50 people died of Covid in the county as of April 10; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:

1. Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death

2. People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths

3. The doubling time of deaths that was used to project to April 22 is based also on reported deaths; if reporting of deaths is delays, the doubling time may appear slower.

I did appreciate the authors' efforts to validate the antibody testing. That's useful information.

I worry that, because these results support their prior beliefs, some readers may take these results at face value and push them for policymakers to use before they have been more widely vetted by the scientific community.

On 2020-04-18 05:16:02, user rodger bodoia wrote:

Deeply flawed methodology. Others have noted (as did the authors) the obvious inherent bias towards those seeking antibody testing (maybe they had symptoms, maybe they knew someone who had symptoms). Also note the bias that is inherent in the method of using Facebook as the messenger with a brief period between posting on FB and the actual testing. We would need significant information on the other behaviors of people who use FB this frequently and whether they are more or less likely to have engaged in practices that would have put them at risk of acquiring the virus.<br /> Back of the envelope "smell test": 48,000 infections and only 69 deaths (as of April 17) is an infection fatality rate of 0.14%. This is inconsistent with Diamond Princess data, even if we adjust for age differences. Also compare with https://www.nejm.org/doi/fu... in which they did UNIVERSAL screening of obstetric patients from March 22 to April 4 in NYC and found 15% positivity of SARS-CoV-2. Without lots of population-weighted adjustments we can interpret this as pretty good evidence of roughly 15% prevalence in NYC (say roughly 1.2 million infections) and roughly 9,000 deaths for infection fatality rate of 0.75%

On 2020-04-18 18:45:15, user S. MonDragon wrote:

Dear Dr. Jay et al.,

I am curious about a couple of other scenarios related to your study. Do those that have SARS-CoV-2 antibodies, also show any other antibodies that might be of particular research interest? And further, how many of these people actually had any symptoms? For example, how many of those who had COVID-19 antibodies also had antibodies for other types of coronaviruses, including SARS-CoV-1. Did the presence or absence of these other antibodies seem to have an effect on symptom severity? I guess what I am asking is, why do some people have such severe symptoms while others can walk around without even knowing that they may have the virus? And, can your samples help us to answer some of these questions?

On 2020-04-19 00:34:15, user SonoranSeeker wrote:

Considering that this virus is extremely contagious, two or three times that of the flu, and considering that this extremely contagious virus was circulating unabated for a relatively long time, this study is probably pretty accurate. It is also in line with the study in Germany and modelling of virus spread based on previous corona virus characteristics. <br /> This could be why there were so many deaths in such a short time. Let's hope it burned hot, but will flare out just as fast.

On 2020-04-19 02:02:27, user defragmentingthecode wrote:

THe CDC's guidelines for reporting Covid19 deaths is "where the disease caused or isassumed to have caused or contributed to death".

I really don't know how accurate any studies are when we don't know how many Covid deaths were assumed, and how many deaths were due to the patient's co-morbidities rather than the presence of virus? Surely, we could have got this bit right?

Here is the CDC link. https://www.cdc.gov/nchs/da...

On 2020-04-19 05:26:28, user chalkful wrote:

You can talk about selection bias, and that’s valid. But nit-picking every part of this study down to the assumptions made about manufacturer specifications seems ridiculous and very hypocritical when similar assumptions were made with RT-PCR tests that were rushed to market with questionable, if any, validation, and which are relied upon to make public policy decisions which dictate the lives of millions, even billions.

Did you apply precisely the same level of detailed, critical analysis and nitpick every minute inconsistency in all the other COVID-related “peer reviewed” studies which were rushed to print, and which were gloom-and-doom?

Why choose to write off all conclusions drawn by a study with such a substantial effect size because of minor statistical errors? As a non-academic, the study appears largely methodologically sound, despite a few flaws, and it is not logical to throw the baby out with the bath water. Even assuming the infection rate is half of what is concluded, that is still significant and the first study, and conclusion, of its kind.

I suspect that, with as with most things COVID-related, much of this is due more to politics than pure intellectual rigor, and that the conclusions drawn by the study shake the foundation of what many believe, which scares them.

On 2020-04-20 02:47:08, user Comfrey's Gone wrote:

Probably related to Dean Karlen's observations below - but in working through the statistical appendix, it seems like the calculation of the standard error is independent of the number of samples (371 or 401) used by the manufacturer/Stanford team to evaluate the number of false positives.

To determine the standard error, the authors first compute the cumulative variance by combining variances from each source of uncertainty (finite sample of respondents of 3,330, finite sample for false positives in the serology test and finite sample for false negatives). These separate variances are the variances of the binomial distribution (p(1-p)), not rescaled by the inverse of the sample size. The authors then take this cumulative variance and divide by the number of respondents (3,330), and apply the square root to arrive at the standard error. (.0039 = sqrt(.034/3330)).

Instead, when the cumulative variance is computed in the equation for Var(Pi) above, I believe that each of the contributing terms should be multiplied by its appropriate 1/N (where N is the relevant sample size, e.g. 3,330 for the Var(q) term, and 371 or 401 for the Var(s) term.)

One way to assess that the 'N' rescaling doesn't seem right is to think about the limit in which the number of respondents being tested is infinite, the sample size for determining the number of false negatives is also infinite, but there is a finite sample (e.g. 401) used to determine the number of false positives. If you trace through the appendix calculation, you'll then find (if I've done it correctly) that the standard error for 'Pi' (the infection rate) would then be zero, although some error certainly should exist, due to the uncertainty in false positives.

Other commenters have also raised concerns about the normality assumption in computing the standard error, but the way in which scaling by sqrt(N) has been applied here has a large impact on the calculation of the standard error and resulting confidence intervals.

On 2020-04-20 03:50:27, user Tomas Hull wrote:

Those who insist on the selection bias of this study: Would you rather see the ads targeting people working in hospitals and covid19 assessment centres, or those providing essential services to those institutions, like mailmen, delivery men, garbage men, cleaning and maintenance, and so on? <br /> How about people in self-isolation, COVID-19 observation and ICU wards? <br /> Would this kind selection bias satisfy anybody?

On 2020-04-20 10:50:57, user Douglas Gilliland wrote:

Peer review rejects this study... https://medium.com/@balajis...

On 2020-04-23 16:10:24, user quillerm wrote:

Why are all the survey participants over 65 and such a large percentage diabetics?

On 2020-04-21 19:40:16, user Brandon B wrote:

Risk of ventilation was 6.9% in HQ + AZ group and 14.1% in no Tx group. That is double. It was stated that these numbers are similar in the article. Not significant?

On 2020-06-23 17:39:31, user Liam Golding wrote:

Hi great work by your team.

I'm curious whether you standardize the log inactivation to untreated masks or to viral stock added. You note that for bacterial contaminants that untreated coupons are compared to treated to obtain log reduction values. But, for example, you note that "For each decontamination method, each sample used for treatment had a corresponding no-treatment control. No-virus blank masks were also included to identify possible contamination." Was the control viral load extracted then compared to treatments to obtain log reduction values, or was a known quantity of viral load added to controls and used to determine the log reduction?

**Edit: you draw mention to this in the Material and Methods.

However, compared to other studies (Mills et al., 2018; Lore et al., 2011) your method of extracting viral load is minimal to say the least. Generally, coupons are cut; placed in a 15/50ml tube with ~ 15ml extraction solution then vortexed/mixed for 20 minutes. Can you comment on why you chose 1 minute vortex with 1.3mL solution over the common OP?

On 2020-04-07 21:56:51, user VesnaV wrote:

The idea of the article is very interesting. But I am afraid that the trends are changing. It would be very useful to update the data on covid-19 and replicate the analysis. Could you please do it? Thanks a lot!

On 2020-06-24 18:16:30, user Gerard Cangelosi wrote:

Nice study, and a very valuable addition. I collaborated on one of the previous studies you cited (Tu YP et al, 2020). May I suggest an alternative explanation for the difference between your findings and ours? You used all-purpose flock swabs, and we used foam swabs. These differences aren't trivial (e.g. see https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.28.20083055v1)"). I would urge you to note this possibility in your manuscript. Thank you!<br /> Jerry

On 2020-06-24 18:56:17, user André GILLIBERT wrote:

Title : Proposal for improved reporting of the Recovery trial<br /> André GILLIBERT (M.D.)1, Florian NAUDET (M.D., P.H.D.)2<br /> 1 Department of Biostatistics, CHU Rouen, F 76000, Rouen, France<br /> 2 Univ Rennes, CHU Rennes, Inserm, CIC 1414 (Centre d’Investigation Clinique de Rennes), F- 35000 Rennes, France

**Introduction**

Dear authors,<br /> We read with interest the pre-print of the article entitled “Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report”. This reports the preliminary results of a large scale randomized clinical trial (RCT) conducted in 176 hospitals in the United Kingdom. To our knowledge it is the largest scale pragmatic RCT comparing treatments of the COVID-19 in curative intent. The 28-days survival endpoint is objective, clinically relevant and should not be influenced by the measurement bias that may be caused by the open-label design. While 2,315 study protocols have been registered on ClinicalTrials.gov about COVID-19, as of June 24th 2020, Recovery is, to our knowledge, the only randomized clinical trial on COVID-19 that succeeded to include more than ten thousands patients. The open-label design and simple electronic case report form (e-CRF) may have helped to include a non-negligible proportion of all COVID-19 patients hospitalized in the United Kingdom (UK). Indeed, as of June 24th 2020, approximatively 43,000 patients died of COVID-19 in hospital in the UK, of whom approximatively 0.24 × 11,500 = 2,760, that is more than 6% of all hospital deaths of COVID-19, where included in the Recovery study.<br /> Having read with interest version 6.0 of the publicly available study protocol (https://www.recoverytrial.n... "https://www.recoverytrial.net/files/recovery-protocol-v6-0-2020-05-14.pdf)") we had hoped for more details in the reporting of methods and results of this trial and take advantage of the open-peer review process offered by pre-prints servers to suggest improving some aspects of the reporting before the final peer-reviewed publication. Please, find below some easy to answer comments that may help to improve the article overall.

**Interim analyses and multiple treatment arms**

The first information would be about interim analyses. The protocol (version 6.0) specifies that it is adaptive and that randomization arms may be added removed or paused according to decisions of the Trial Steering Committee (TSC) basing its decision on interim analyses performed by the Data Monitoring Committee (DMC) and communicated when “the randomised comparisons in the study have provided evidence on mortality that is strong enough […] to affect national and global treatment strategies” (protocol, page 16, section 4.4, 2nd paragraph). The Supplementary Materials of the manuscript specifies that “the independent Data Monitoring Committee reviews unblinded analyses of the study data and any other information considered relevant at intervals of around 2 weeks”. This suggests that many interim analyses may have been performed from the start (March 9th) to the end (June 8th) of the study.<br /> Statistically, interim analyses not properly taken in account generate an inflation of the type I error rate which may be increased again by the multiple treatment arms. Methods such as triangular tests make it possible to control the type I error rate. Most methods of control of type I error rate in interim analyses require that the maximal sample size be defined a priori and that the timing and number of interim analyses be pre-planned. This protocol being adaptive, new arms were added, implying new statistical tests in interim analyses, and no pre-defined sample size as seen in page 2 of the protocol: “[...] it may be possible to randomise several thousand with mild disease [...], but realistic, appropriate sample sizes could not be estimated at the start of the trial.” This make control of the type I error rate difficult. The fact that the study has been stopped on the final analysis as we understand from the current draft rather than interim analysis does not remove the type I error rate inflation. The multiple treatment arms lead to another inflation of the type I error rate.<br /> The current manuscript does not specify any procedure to fix these problems. The Statistical Analysis Plans (SAP) V1.0 (in section 5.5) and V1.1 (in section 5.6) specify that “Evaluation of the primary trial (main randomisation) and secondary randomisation will be conducted independently and no adjustment be made for these. Formal adjustment will not be made for multiple treatment comparisons, the testing of secondary and subsidiary outcomes, or subgroup analyses.” and nothing is specified about interim analysis. Therefore, we conclude that no P-value adjustment for multiple testing has been performed, neither for multiple treatment arms nor for interim analysis. If an interim analysis assessing 4 to 6 treatment arms at the 5% significance level has been performed every 2 weeks from march to June, up to 50 tests may have been performed, leading to major inflation of type I error rate. In our opinion, the best way to assess and maybe fix the type I error rate inflation, is to report with maximal transparency every interim analysis that has been performed, with the following information:<br /> 1. Date of the interim analysis and number of patients included at that stage<br /> 2. Was the interim analysis planned (e.g. every 2 weeks as planned according to supplementary material) or unplanned (e.g. due to an external event, for instance the article of Mehra et al about hydroxychloroquine published in The Lancet, doi:10.1016/S0140-6736(20)31180-6), and if exceptional, why?<br /> 3. Which statistical analyzes, on which randomization arms, have been performed at each stage <br /> 4. If predefined, what criteria (statistical or not) would have conducted to early arrest of a randomization arm for inefficiency and what criteria would have conducted to arrest for proved efficacy?<br /> 5. If statistical criteria were not predefined, did the DMC provide a rationale for his choice to communicate or not the results to the TSC? If yes, could the rationale be provided?<br /> 6. The results of statistical analyzes performed at each step<br /> 7. The decision of the DMC to communicate or not the results to the TSC and which results have been reported as the case may be<br /> The information about interim analyses and multiple randomization arms will help to assess whether the inflation of type I error rate is severe or not. A post hoc multiple testing adjustment, taking in account the many randomized treatments and interim analyses, should be attempted, and discussed, even though there may be technical issues due to the adaptative nature of the protocol.

**Adjustment for age**

An adjustment for age (in three categories <70 years, 70-79, >= 80 years, see legend of table S2) in a Cox model was performed for the comparison of dexamethasone to standard of care in the article. This adjustment was not specified in the version 6.0 of the protocol but was, according to the manuscript “added once the imbalance in age (a key prognostic factor) became apparent”. This is confirmed by the addition of a words ““However, in the event that there are any important imbalances between the randomised groups in key baseline subgroups (see section 5.4), emphasis will be placed on analyses that are adjusted for the relevant baseline characteristic(s).” in section 5.5 page 16 of the SAP V1.1 of June 20th compared to the SAP V1.0 of June 9th which specified a log-rank test. The SAP V1.0 of the 9th June may have been written before the database has been analyzed (data cut June 10th) but the SAP of the 20th has probably been written after preliminary analysis have been performed. This is consistent with the words “became apparent” of the manuscript. Therefore, in our opinion, this adjustment must be considered as a post hoc analysis rather than as the main analysis. Moreover, even though the SAP V1.1 specifies that an “important imbalance” will lead to an “emphasis” on adjusted analyses, it does not change the primary analysis (see section 5.1.1 page 14). It is not clear what “important imbalance” means. To interpret that, we will perform statistical tests to assess balance of key baseline subgroups specified in SAP V1.1 (see section 5.4):<br /> 1. Risk group (three risk groups with approximately equal number of deaths based on factors recorded at randomisation). Its distribution is shown in figure S2. A chi-square tests on the distribution of risk groups in Dexamethasone 1255/500/349 and Usual care 2680/926/715 groups, lead to a P-value=0.092. A chi-square test for trend yields a P-value equal to 0.23.<br /> 2. Requirement for respiratory support at randomisation (None; Oxygen only; Ventilation or ECMO). P-value=0.89 for chi-square test and P-value=0.86 for chi-square for trend.<br /> 3. Time since illness onset (<=7 days; >7 days). P-value=0.17<br /> 4. Age (<70; 70-79; 80+ years). P-value=0.016 for chi-square test, p=0.019 for chi-square test for trend<br /> 5. Sex (Male; Female). P-value=0.97 for chi-square test<br /> 6. Ethnicity (White; Black, Asian or Minority Ethnic). No data found.<br /> The criteria to define “important imbalance” seems to be statistical significance at the 0.05 threshold, however that should have been stated and tests for all other variables should have been provided too.<br /> First, this adjustment, from a theoretical point-of-view, was not necessary since the study was randomized; if the exact condition of imbalance triggering the adjustment was pre-specified in the protocol or SAP before the imbalance was known, it could induce a very slight reduction of the type I error rate and power. However, as it was performed when the imbalance was known, there is a risk that the sign of the imbalance (i.e. higher age in the dexamethasone group) have influenced the choice of adjustment. Indeed, an adjustment conditional to a higher age in the dexamethasone group will increase the estimated effect of dexamethasone in these conditions, and so, provide an inflation of the type I error rate. If the same conditional adjustment were further considered for other prognostic variables, the inflation could even be higher. <br /> Unless there is strong evidence that the amendment to the SAP was performed without knowledge of the sign of the imbalance (higher age in the dexamethasone group), we suggest that the primary analysis be kept as originally planned, without adjustment, and that the age adjustment be performed in a sensitivity analysis only. The knowledge of the sign of the unbalance is unclear in the last version of the SAP (V1.1, June 20th) and in the manuscript. In addition, in an open label trial, it is always better to stick to the protocol.

**Results in other treatment arms**

The manuscript specifies that “the Steering Committee closed recruitment to the dexamethasone arm since enrolment exceeded 2000 patients.” It is not stated whether any other treatment arm has exceeded 2000 patients or not and whether the study is still ongoing. Results of treatment arms that have been stopped should be provided (all arms having enrolled more than 2000 patients?). If not, the number of patients randomized in other treatment arms should, at least, be reported. If the study is completely stopped, all treatments should be analyzed and reported, unless there is a specific reason not to do so; that reason should be stated as the case may be. This data would be useful to provide evidence on other molecules. It would also clarify the number of statistical tests that have been performed or not, providing more information about the overall inflation of alpha risk.

**Sample size**

The paragraph about the sample size suggests that inclusions were planned, at some time, to stop when 2000 patients were included in the dexamethasone arm. The amended protocol (May 14th), the SAP V1.0 (June 9th) and the SAP V1.1 (June 20th, 4 days after the results have been officially announced) all have a paragraph about the sample size but all specify that the sample size is not fixed and none specify any criteria of arrest of the research based on sample size. There are 2104 patients included in this arm, which is substantially larger than the target of 2000 patients. The exact chronology and methodology should be clarified: when was the sample size computed and what was the exact criteria to arrest the research? Could the document (internal report?) related to this sample size calculation and statistical or non-statistical decision of arrest of the research be published in supplementary material?<br /> Indeed, assessment of the type I error rate requires knowing exactly when and why the research has been arrested: arrest for low inclusion rate of new patients or for reaching target sample size cannot be interpreted the same as arrest for high efficacy observed on an interim analysis.

**Future of the protocol**

With the new evidence about dexamethasone, the protocol will probably be stopped or evolve. The future recruitment may slow as the peak of the epidemic curve in United Kingdom is passed. The past, present and future of the protocol needs also to be known to assess the actual type I error rate. Indeed, future analyses, that have not yet been performed influence the overall type I error rate. That is why we suggest that author’s provide the daily or weekly inclusion rate from March to June and discuss the future of the study.

**Loss to follow-up**

Table S1 shows that the follow-up forms have been received for 1940/2104 (92.2%) patients of the dexamethasone group and 3973/4321 patients of the usual care group (91.9%). The patients without follow-up forms (8.5% overall) may either be lost to follow-up or have been included in the 28 last days before June 10th 2020 (data cut). The manuscript mentions that 4.8% of patients “had not been followed for 28 days by the time of the data cut”, suggesting that 8.5%-4.8% = 3.7% of patients are lost to follow-up, but that is our own interpretation. We suggest that authors report the actual number of loss to follow-up and how their data have been imputed or analyzed. The number of loss to follow-up may differ for different outcomes. For instance, if the Office of National Statistics (ONS) data has been used for vital status assessment, there should be no loss to follow-up on that outcome.

**Vital status**

The current manuscript only specifies the data of the web-based case report (e-CRF) form, filled by hospital staff, as source of information, suggesting that it is the only source of information about the vital status. The document entitled “Definition and Derivation of Baseline Characteristics and Outcomes” provided at https://www.recoverytrial.n... specifies many other sources. For instance, the vital status had to be assessed from the Office of National Statistics (ONS). Other sources, including Secondary Use Service Admitted Patient Care (SUSAPC) and e-CRF could be used for interim analysis. The ONS was considered as the defining source (most reliable). Whether the ONS data has been used or not should be clarified. If the ONS data have been used, statistics of agreement of the two data sources (e-CRF and ONS) may be provided to help assessing the quality of data. If the ONS data have not been used, this deviation from the planned protocol should be documented.<br /> The manuscript as well as the recovery-outcomes-definitions-v1-0.pdf file specifies that the follow-up form of the e-CRF is completed at “the earliest of (i) discharge from acute care (ii) death, or (iii) 28 days after the main randomisation”. If the follow-up form is not updated further, patients discharged alive before day 28 (e.g. day 14) may have incomplete vital status information at day 28. The following information should be specified:<br /> 1. Whether the follow-up form of the e-CRF had to be updated by hospital staff at day 28 for these patients<br /> 2. If response to (1) is yes, whether there was a means to distinguish between a lost to follow-up at day 28 (form not updated) and a patient discharged and alive at day 28 (form updated to “alive at day 28”)<br /> 3. If response to (2) is yes, how many patients discharged before day 28 were lost to follow-up at day 28<br /> 4. If response to (2) is yes, how has their vital status at day 28 been imputed or managed in models with censorships (log-rank, Kaplan-Meier, Cox)<br /> Of course, this information is really needed if the ONS and SUSAPC data have not been used.<br /> The quality of the vital status information is critical in such a large scale open-label multi-centric trial, because there is a risk that one or more center selectively report death, biasing the primary analysis.

**Inclusion distribution by center**

A multicentric study provides stronger evidence than a single-center study but sometimes, few centers include most patients, with a risk of low-quality data or selection bias. The very high number of included patients in the Recovery trial suggests that many centers included many patients but the distribution of inclusions per center could be reported.

**Randomization**

The protocol specifies that “in some hospitals, not all treatment arms will be available (e.g. due to manufacturing and supply shortages); and at some times, not all treatment arms will be active (e.g. due to lack of relevant approvals and contractual agreements).” This is further clarified in the SAP V1 (section 2.4.2 Exclusion criteria, page 8) by the sentence “If one or more of the active drug treatments is not available at the hospital or is believed, by the attending clinician, to be contraindicated (or definitely indicated) for the specific patient, then this fact will be recorded via the web-based form prior to randomisation; random allocation will then be between the remaining (or indicated) arms.” Showing that randomization arms may be closed on an individual basis, when the patient is included, with the argument of contraindication or definitive indication. It seems that the “standard of care” group could not be removed and that at least another randomization arm had to be kept as suggested by the words “random allocation will then be between the remaining arms (in a 2:1:1:1, 2:1:1 or 2:1 ratio)” in section 2.9.1 page 11 of the SAP V1.0. Even exclusion of a single randomization arm can lead to imbalance between groups. For instance, if physicians believed that a treatment was contraindicated for the most severe patients, only non-severe patients could be randomized to the treatment’s arm, while most severe patients would be randomized to other arms. Several things can be done to assess and fix this bias. First, report how many times this feature has been used and which randomization arms have been most excluded. If it has been used many times, provide the pattern of use that help to assess whether this is a collective measure (e.g. 2-weeks period of shortage of a treatment in a center ? no major selection bias) or individual measure. If its use has been rare, a sensitivity analysis could simply exclude these patients. If it has been frequent, we suggest a statistical method to analyze this data without bias, based on the following principles: patients randomized between 3 randomization arms A, B and C (population X) are comparable for the comparisons of A to B. Patients randomized between A, B and D (population Y), are comparable for the comparisons of A to B. Population X and population Y may differ but, inside each population, A can be compared to B. Therefore, the within-X comparison of A to B and within-Y comparison of A to B are both valid and can be meta-analyzed to assess a global difference between A and B. This can be simply done with an adjustment on the population (X or Y) in a fixed effects multivariate model. Pooling of X and Y populations should not be performed without adjustment.<br /> A second problem with randomization exists although the dexamethasone arm is the least affected. Randomization arms have been added in this adaptative trial. When a new randomization arm is added, new patients may be randomized to this arm and fewer patients are randomized to other arms. Consequently, the distribution of dates of inclusion may differ between groups. This may have some impact on the mortality at two levels: (1) the medical prescription of hospitalization may have evolved as the epidemic evolved, with hospitalization reserved to most severe patients at the peak of epidemic and maybe wider hospitalization criteria at the start of epidemic and (2) evolution of patients included in the Recovery trial. Indeed, even if centers should have included as many patients as possible as soon as their inclusion criteria were met, it is possible that they have only included part of eligible patients and that this part evolved with time. This bias can be easily assessed and fixed: the curves of inclusions in the different arms and mortality rate in the Recovery trial can be drawn as a function of date (from March to June) and an adjustment on date of inclusion may be performed in a sensitivity analysis.

**Conclusion**

Recovery is the study with the best methodology that we have seen on COVID-19 treatments in curative intent and we salute the initiative of publishing transparently the protocol, its amendments, the statistical analysis plan and the first draft of the report. We hope that our reporting suggestions will be taken in account in the final version of the paper. We think that discussing these points will qualify the interpretation of results, further improve the transparent approach adopted by designers of the study and improve the reliability of the conclusions. We expect a high-quality reporting of these final results, with full transparency on interim analyses, statistical analysis plans and statistical analysis reports. We hope that these comments are helpful and again we acknowledge that this study is not solely outstanding in terms of importance of the results but is also a stellar example for the whole field of therapeutic research. We invite other researchers to provide comments to this article to engage in Open Science.

On 2020-06-25 04:04:22, user Greg WHITTEN wrote:

Thank you for your work. I am curious, however, about some parts of your article.

First, I read your paper and could not see where you tried to control for the introduction of other virus-containment measures such as school closures, lock-downs, and physical distancing. Did I miss something in your paper?

Second, I have a question about your model #4 on page 9. You wrote "All<br /> regression coefficients were statistically significant in this model." The coefficient for the non-mask wearing rate in late April and early May is significant but negative. I.e., not wearing a mask in late April and early may reduces deaths on May 13th. Do you have any thoughts about this?

Third, did you consider performing a panel regression using deaths on all days, say, starting from March 31st (about 2 weeks after the March mask non-wearing rate) instead of relying just on deaths from May 13? Although you did explain why you chose May 13th, it may be better to use all death dates after, say, the incubation period for the virus.

Fourth, your section "Prediction of mask non-wearing rates" suggests that your regression analysis suffers from multicollinearity. Do you have any concerns about this?

On 2020-06-25 11:29:20, user MAGB wrote:

Your basic reproductive number of 2.68 based on early Chinese data is at odds with the effective reproduction number of less than one in all Australian states by Easter, as tweeted by James McCaw. His data indicate that voluntary controls and border closures had the epidemic well under control before lock-downs had any effect.

On 2020-06-26 16:11:55, user Kevin McKernan wrote:

Very interesting work. There are many data points suggestion earlier introduction.<br /> It would strengthen the manuscript if you could provide the CQs for your NTC for the late PCRs.

I would also suggest sequencing the amplicons to see if these are ancestral or SARS-CoV-2.

https://youtu.be/guMqyrmni4I

https://youtu.be/2SfXZa7-JKM

On 2020-04-08 22:31:44, user Mansour Tobaiqy wrote:

I am glad to say that our manuscript Therapeutic Management of COVID-19 Patients: A systematic review has now accepted for publication at the Infection Prevention in Practice @IPIP_Open the Official Journal of the Healthcare Infection Society @HIS_infection

The last version will be available soon at their site. Thank you very much medRxiv for sharing our SR to a great and large audience .

On 2020-04-10 22:02:12, user Todd Johnson wrote:

Have any of the causal inference researchers at Harvard taken a look at this? Do we know enough to create a few candidate causal DAGs to know what to adjust for?

On 2020-07-01 14:02:04, user Dude Dujmovic wrote:

I don't believe this research has much in it. I think there is a richer social context for people vaccinated for Flu and that social context makes them less susceptible to COVID-19. For example if person lives in society where standards of care are higher then that person will have a longer lifetime and will also be more likely vaccinated against various diseases. You only accounted for education and that is not enough. But your research data does show connection between education and risk of death in COVID-19.

On 2020-08-15 14:01:48, user Dom_Pedulla wrote:

Joao not only had Dude made some very good points, but in observational trials like this, everything depends on the nitty gritty data. I notice the huge qualifier "recent" in the results sentence, noting that carefully since in many studies these kinds of adjectives disclose or hint at certain erroneous tendencies or conclusions in even in "meticulous peer-reviewed studies". I am requesting the paper to analyze for myself, and suspect strongly that what it may show is a "benefit" for only the very recently vaccinated, and that either long after it either ends up being a net liability as regards COVID death risk, or that the timing isn't possible to discern because the investigators avoided studying all but the recently vaccinated.

We'll see.

On 2020-07-02 15:42:32, user Kamran Kadkhoda wrote:

The entirety of covid serology remains questionable with lack of clinical usefulness; the specimen type therefore is irrelevant...

On 2020-07-02 18:00:55, user Wouter wrote:

Same model, new simulations: https://academic.oup.com/ci...

On 2020-07-05 20:02:42, user Rich Nunziante wrote:

There’s a word missing in the first paragraph of the abstract: “Of the 9 locations, 3 had one or employees infected with SARS-CoV-2,...” Should that be “one or two” since later you mention “both”?

On 2020-06-21 12:49:38, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-07-14 15:00:51, user Chyke Doubeni wrote:

The title should reflect the multicomponent nature of the intervention so that it is clear to readers that it used CHW to help people navigate the engagement

On 2020-04-17 14:41:47, user the.plummers@talktalk.net wrote:

Note that the link to the model on the front of the paper is out of data. It should be https://github.com/arp23/Ba... . The link on this medRxiv page is correct. Thanks.

On 2020-04-17 18:17:20, user LASD wrote:

So...uh...what about Sweden? Have yet to see any reasonable explanation for why the lack of lockdown there didn't lead to catastrophic consequences and bodies piled up in the streets?

Significantly lower number of confirmed cases/deaths than Switzerland and all the other major western European countries, Belgium, etc.

On 2020-04-19 15:53:35, user JGaltbna wrote:

Nothing happens “right now”. I suggest actually reading the WH plan to reopen and what has to happen before anything is “relaxed” per policy. 3 phases, each lasting at least 14 days? Ring a bell? The only restrictions being eased now are things that should never have been restricted like walking on a beach. The danger isn’t the policy but that people ignore the policy.

On 2020-11-25 05:08:10, user ArthurConanDoyle wrote:

Layman here, w/Covid. Wondering why you don't use sputum for greater accuracy?

Of course, it's not as easy or ubiquitous as saliva, but maybe a sample option?<br /> The major point being accuracy is almost everything, other factors count, but...

On 2020-11-27 19:55:21, user John Butler wrote:

There seems to be either something wrong with the risk calculator or the paper text. If I choose White Female age 70-74, no comorbidities, it says 18.9% higher. I take it that means "multiply the base rate time 1.189". If I switch that to "male" it reports 119.1% higher, which would, to be consistent with the female, mean "multiply the base rate times 2.191". However, if I select Hispanic Male, 80-84 with Chronic Kidney Disease, it report 601.8%, the text reports "6 times higher". All this suggests that the White Female 70-74, as an example, is inconsistent with the form of the others.

On 2020-12-01 04:48:42, user Sandeep B wrote:

Another one !

https://www.statnews.com/20...

On 2020-12-02 11:30:50, user Rafael Moraes wrote:

This preprint has been published in a peer-review journal:

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242251

On 2020-12-02 20:32:21, user Roberto Etchenique wrote:

Great work !! We had done the same procedure, but a bit cruder, for City of Buenos Aires, Argentina. Our results for our country (not published) are coincident with these ones. https://www.medrxiv.org/con...

On 2020-12-10 00:06:10, user Amandeep Goyal wrote:

What kind of scores or testing was used to diagnose polyneuropathy ?

What tests were used if polyneuropathy was due to Diabetes or Amyloidosis ?

On 2020-12-10 17:00:23, user Susan Bewley wrote:

Thanks Russell & team. Would it be possible for you to share the following on #medRxiv too? (i) the research information leaflet you describe, (ii) the informed consent form, (iii) the statistical plan mentioned as S2. Best wishes

On 2020-12-14 08:49:55, user Patrick Schmidt wrote:

Interesting connection between network models and standard inference on contact tracing data.

I have a paper showing that the tendency of superspreading can be estimated without contact tracing data from aggregated surveillance data alone. In line with the results here, I estimate a dispersion of 0.61 (95%-CI: [0.49, 0.77]) for Germany in spring 2020.

https://arxiv.org/pdf/2011....

On 2020-12-20 22:06:38, user Sam Smith wrote:

Is it possible to begin this treatment too early or too late, or to continue for too long?

On 2020-12-29 00:37:11, user Olga Matveeva wrote:

Several recent preprints support some of this manuscript findings.<br /> 1. Authors from Sweden and China in a study entitled “Pulmonary stromal expansion and intra-alveolar coagulation are primary causes of Covid-19 death” demonstrated that “The virus was replicating in the pneumocytes and macrophages but not in bronchial epithelium, endothelial, pericytes or stromal cells. doi: https://doi.org/10.1101/202...<br /> 2. Researchers in Brasil investigated SARS-CoV-2 infection of PBMCs and found that in vitro infection of whole PBMCs from healthy donors was productive of virus progeny. They also found that “SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from COVID-19 patients, and less frequently in CD4+T lymphocytes” The preprint is entitled “Infection of human lymphomononuclear cells by SARS-CoV-2”. <br /> doi: https://doi.org/10.1101/202...<br /> 3. SARS-CoV-2 infection of macrophages and some other immune cells in deceased patients was suggested in other autopsy related preprint entitled “Broad SARS-CoV-2 cell tropism and immunopathology in lung tissues from fatal COVID-19” doi: https://doi.org/10.1101/202... The study was done by US researchers from Pittsburgh. <br /> 4. Researchers in France demonstrated “that SARS-CoV-2 efficiently infects monocytes and macrophages without any cytopathic effect.” Their findings are reported in the preprint entitled “Monocytes and macrophages, targets of SARS-CoV-2: the clue for Covid-19 immunoparalysis” doi: https://doi.org/10.1101/202...

On 2020-12-29 21:12:44, user Meerwind7 wrote:

It seems these evaluations assume that all non-pharmaceutical intervention and prevention measures (e.g. masks and “lockdowns”) would be abolished once the vaccinations start. In a different approach, these measure would be upheld for a while, for example such as to limit prevalence to a certain level for some time, or to limit the number of overall deaths. One such target could be called “partial herd immunity”, which is achieved when the combination of partial vaccination and some amount of precaution measures would in combination be sufficient to assure the reproduction factor not to exceed 1 or to achieve quick shrinking of infection numbers. The combination of some vaccine plus non-pharmaceutical interventions thus would have an effect similar to full herd immunity that is achieved when recurrent infection is avoided with fully “normal” life.

If there was only one type and scope of non-pharmaceutical intervention, an objective could be formulated as “how to minimize the duration of that intervention, when a certain maximum number of deaths (or of severe illness) shall be maintained”, taking into account vaccine doses become available slowly or other restrictions apply. A further objective should be to minimize or cap the number of vaccinated persons that are exposed to virus, because each such expose gives mutants an opportunity to break the barrier from vaccination, just like an evolutionary training.

It would also be possible to optimize vaccine distribution and non-pharmaceutical intervention while setting a target for a particular age group. Even if an upper limit of the death count of, for example, people of 75+ years was a binding target, and some non-pharmaceutical intervention is available, it may be better to vaccinate younger people first, to reduce overall transmission more quickly and then be able to “open” the society quicker, than if 75+ obtain vaccine first.

In further modeling, the extent (effect strength) of the non-phamaceutical measure could be gradually increased, while maintaining a goal like low nomber of overall deaths, lost lifetime or deaths of old people. I believe there could be some point where the results suddenly switch from vaccines for the old to vaccines for the young, and that beyond that point, the duration of the intervention could suddenly be reduced in a non-continuous way while upholding aggregate goals.

On 2020-12-30 01:49:12, user Franko Ku wrote:

Perhaps you should start over based on others' comments..<br /> Only one dose? Should be calcifediol?<br /> What were measured levels of Vit, D in those that received placebo?<br /> Many other studies show those with very low hormone (not a "vitamin" D have much more risk of dying.<br /> https://www.researchsquare....<br /> https://link.springer.com/a...<br /> https://www.sciencedirect.c...<br /> https://www.ncbi.nlm.nih.go...<br /> https://medium.com/microbia...

Needed for Prevention - your paper will prevent some from supplementing as Dr Fauci said he does.:<br /> https://www.healthline.com/...

On 2020-11-18 16:18:54, user LB wrote:

Please add, in the Limitations, a comment about the fact that, "The mean time between the onset of symptoms and randomization was 10.2 days." It is quite possible that by the time the vitamin D levels were raised, the "cytokine storm" was already well underway. Thank you!

On 2021-01-05 22:41:19, user Troy Richlen wrote:

An important variable that this and other studies have not been able to adequately incorporate into this analysis is the effect of comorbidities on life expectancy of COVID-19 deaths which is due to a lack of appropriate statistical information.<<<br /> This is calculating the delta of the age of death due to Covid versus the average age of death for the population not the population of people who average 2.6 comorbidities. People who are obese, have diabetes and other significant health issues also will have a negative offset from the average age of death.

On 2021-01-15 14:43:05, user Rafael M wrote:

I would like to know the false positivity of PCR compare with the result published by the press .

On 2020-09-11 14:12:07, user Kamran Kadkhoda wrote:

Great paper but it is pivotal to highlight that correlate of protection is ONLY inferred from prospective vaccine efficacy trials instead of from convalescent cases... <br /> The inflation in MBC population shown here may very well partly be from the common CoVs

On 2020-09-13 07:31:07, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-09-14 11:45:41, user Andrew Boswell ???????????? wrote:

"We found that an increase of only 1 ????g/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate"

Is your COVID evidence actually reflecting a more general extreme sensitivity to PMs across underlying respiratory conditions which has been detected through the lense of the COVID research. I found this tweet (https://twitter.com/AliNour... "https://twitter.com/AliNouriPhD/status/1296554508684754945?s=20)") where Dr Ali Nouri says that the same effect has also been observed for other respiratory viruses like Influenza and SARS-1, and reflects the impacts of PMs to the underlying respiratory and cardiac system.

Have you looked into this with your research?

Is there other studies out there which suggests COVID is a lense to see other more underlying effects?

On 2020-09-16 21:36:10, user Qunfeng Dong wrote:

An updated version of this manuscript is now accepted for publication at JAMIA (Journal of the American Medical Informatics Association)

On 2020-09-17 11:50:43, user Brian Kennedy wrote:

Semi - comment, also a question.

I am an economist living in Bangkok, Thailand, so this is pretty far out of my area of expertise. Thailand was the first country to get the virus outside China, but it never took off here, at this date still less than 4,000 total cases, and 60 deaths. I think there were a variety of things that led to this, but clearly early and near universal mask usage was part of it.

Your paper looks very interesting, but I could not really follow all the math. So I will trust you on it :)

My question is one of emphasis by public health officials in the U.S. Why has there not been a push on the issue of Viral Load? It seems to me that it is very important concept - even if using the mask doesn't always prevent you from getting infected, it will still reduce the viral load, giving your body more time to deal with the virus, significantly increasing your bodies chances of fighting it off.

Why has this issue been (it seems to me) largely absent from the public sphere, and from the arguments public health officials use to promote mask usage? Note if it has been there and I am missing it from far away, just say so.

Thank you for helping a laymen in your field understand :)

On 2020-09-22 20:47:31, user Scandinavian Journal wrote:

Outpatient treatment has not been the focus of almost any clinical study and this may be among the first that examine the side effects. <br /> Since the doses are very low for the hydroxychloroquine in early treatment it is no surprise that side effects is less of an issue. <br /> In fact a popular treatment is where zinc is the active component that slows virus replication and where the role of hydroxychloroquine is to act as a so called zinc ‘ionophore’ where it works to increase zinc uptake into cells. Early treatment really show so much potential and where the alarms of danger seems based on improper data for outpatient use.

Those hospital studies that gave overdose treatments to seriously ill patients showed several side effects and was incorrectly taken to represent the risks for ALL Covid-19 usage.

On 2020-09-27 22:00:20, user nan wrote:

It would be be helpful to know the dosage.

On 2020-09-24 14:24:26, user Uri Goldsztejn wrote:

The source code is available on Github:<br /> https://github.com/uri-gold...

On 2020-09-28 09:44:58, user markd wrote:

• very low copies number for air samples, would like to know the LOD for the detection method(s)
• 300l/' x 10' = 3mc, 50l/' x 10' = 0.5mc - why didn't you keep analysis volume the same across sites/samplers?
• can you report on the state/condition of ventilation in the sampled rooms?

On 2020-09-29 09:17:54, user Carlo Wilke wrote:

The article has been published in EMBO Molecular Medicine:

Wilke, C. et al. Neurofilaments in spinocerebellar ataxia type 3: blood biomarkers at the preataxic and ataxic stage in humans and mice. EMBO Mol Med, 2020.<br /> https://doi.org/10.15252/em...

On 2020-09-30 15:25:51, user Sinai Immunol Review Project wrote:

Very interesting paper!

Main Findings<br /> In this preprint, Zietz and Tatonetti explore the relationship between blood type and risk of SARS-CoV-2 infection, disease severity, and mortality. Using data from the electronic health records (EHR) of 1,559 patients who presented with suspected COVID-19 (with only 682 who tested SARS-CoV-2 positive) at New York Presbyterian Hospital (NYP), they analyzed four outcome pairs. Two pairs were used to test risk for infection: i) positive for infection vs negative for infection and ii) positive for infection vs general patient population. Another pair was used to test for infection severity: iii) positive patients intubated (179) vs positive patients not intubated. The last pair was used to test risk of infection-related death: iv) deceased vs surviving patients. As a measure of exposure, they used ABO blood type (A, B, AB, or O) alone or with Rh factor. In total, they generated eight contingency tables, two for each outcome pair, one with Rh and one without. Blood type was found to be significantly associated with SARS-CoV-2 infectivity after chi-squared analysis of positively vs negatively tested patients (p=0.006 for ABO system and p=0.031 for ABO+Rh system). To identify specific blood types that may predict viral test outcomes, they specifically tested each blood type against those of a different blood type for all four outcome pairs tested in the chi-squared analysis. Fisher-exact test showed that a significantly higher proportion of patients with the blood type A tested positive for the virus, and a lower proportion of patients with O and AB tested positive (p=0.009, 0.036, 0,033 respectively). When the Rh factor was included in the analysis, Rh-positive patients with the blood type A were at a 38.2% higher risk for testing positive (p=0.004), while those with the blood type O were at a 21.0% lower risk (p=0.024). Furthermore, they performed a meta-analysis by pooling data from NYP and Zhao et al’s data from China, which substantiated the findings on blood types A and O in a random-effects analysis that compared the positively infected patients with the general populations of NYP (USA), Wuhan, and Shenzhen (China) (OR=1.164, p=0.0291, for A, and OR=0.7252, p=0.0012 for O). This analysis also revealed a new increased risk of testing positive for those with blood type B (OR=1.1101, p=0.0361). Logistic regression models confirmed that although other risk factors such as diabetes, age, and obesity correlate with certain blood types, adding the blood type as a variable to the model significantly strengthens the prediction for SARS-CoV-2 positive versus negative outcome. On the other hand, blood type was not found to be a risk factor for disease severity or mortality in any of the analyses.

Limitations<br /> The study should be considered in the context of its limitations. Firstly, blood type-disease association was significant when comparing patients who tested positively for COVID-19 to those who tested negative, but the result was not replicated when comparing positively-tested patients to the general patient population. As the authors note, only a specific population received testing for COVID-19 while the majority of the patients in the EHR system were never tested, which could explain the discrepancy. Another related limitation is that the sample meant to be representative of the general population consisted only of people in NYP’s EHR database, which may be biased toward a specific population, and it is therefore unclear if the results would replicate in another cohort. Additionally, the finding that AB blood type is associated with lower risk of infection can only be taken as preliminary; the sample size was quite small (only 4.4% of the cohort had that blood type), and the result was not replicated in the meta-analysis with the data from China. Furthermore, the analyses that included Rh factor, the sample sizes for all Rh-negative subtypes were also small, and there were no patients with AB-negative blood who tested positive for the virus. This highlights the necessity for larger cohorts from multiple sites, but the preliminary results are promising.

Significance<br /> This preprint on NYP patients supports the previous results by Zhao et al on Chinese Wuhan and Shenzen patients that showed that individuals with the blood type A are at a greater risk of testing positive while those with type O are at a lower risk. As the authors reported, blood type distribution is different in NYP than in China, this substantiates their results and indicates it may be replicable in other geographical and ethnic populations despite blood type heterogeneity. Furthermore, they provide a more detailed picture through a meta-analysis with both NYP and China data, and include the Rh factor in the NYP analysis. Notably, they introduce new findings: a decreased risk for testing positive for those with AB blood in the NYP-only analysis, and an increased risk in those with blood type B in the pooled analysis. With the use of convalescent serum as a disease therapeutic, the knowledge that those with A-positive and B blood types may be at an increased risk of contracting COVID-19 can help ensure that sufficient amounts of plasma donors are compatible with that blood type. Finally, the study shows that blood type is not a significant predictor of disease prognosis in those infected with SARS-CoV-2, highlighting the need for other immunological and serological predictors of disease severity and mortality.

Credit<br /> Reviewed by Miriam Saffern as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-09-30 16:10:46, user Dan Housman wrote:

The paper is interesting although I would have expected to see some associations between genetic SNPs/genes etc, and certain 'eating types'. There is only one mention I saw of a single gene. Is that type of analysis in the works? I'd be interested to see such an association study.

On 2020-10-06 12:01:01, user Jani Ruotsalainen wrote:

I'm teaching systematic review methods on two occasions this autumn and I'm using this article as an example to be evaluated using the AMSTAR II tool. And yes, I'm doing this even though the authors do not in fact identify their work as a systematic review. My point is to show how a meta-analysis (MA) without all the supporting structures of a thorough systematic review does not really make much sense. AMSTAR II has 16 domains that one scores Yes or No. In some cases it also allows also Partial yes and some items might not be applicable when a review does not include MA. However, in the end there is no overall sum score. Anyway, according to my assessment, the manuscript as it is now scored two instances of Yes and fourteen instances of No. In other words, it didn't do too well. The biggest cause of problems is, in my opinion, the lack of a protocol published a priori that would have established the methods to be used in sufficient detail. Now it is impossible to tell if the authors deviated from their original plans along the way and what effect this might have had on their findings. Other problems include not using a satisfactory technique to assess the risk of bias of results extracted from included studies or its possible effects on results obtained with MA. The description of included studies is minimal and the description of excluded studies is nonexistent. There are also issues with the MA itself (proficiently examined by Jesper Kivelä on Twitter: https://twitter.com/JesperK... "https://twitter.com/JesperKivela/status/1291697936842338305)") and more. I'm happy to share my full assessment with the authors.

On 2020-10-06 15:42:18, user T_Rogers wrote:

How do we know that NR was the effective factor?? Perhaps it was the other ingredients in the mixture. Also, only 71 patients with mean age of 35 and limited to no co-morbidities. IOW, exactly the profile that would be expected to recover. So, good result, but inconclusive as to efficacy of NR.

On 2020-10-07 11:50:31, user Zed wrote:

it is confirming this meta-analysis published in CMI: https://www.clinicalmicrobi...

Maybe authors can discuss the main differences and/or similarity?

On 2020-10-09 12:40:32, user Zed wrote:

ooh nice paper ! <br /> The conclusion is pretty much similar to this meta-analysis: https://www.clinicalmicrobi...

On 2020-10-14 20:35:53, user BannedbyN4stickingup4Marjolein wrote:

If "A fraction of the population may also already be intrinsically resistant to infection as a consequence of high functioning innate immunity" as the paper claim, how is it that infection rates of c. 85%, with the potential to rise further upon further exposure (for example https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.08.13.20173161v1)"), have been observed in homogenous populations?

Such phenomena should surely be referenced in the authors' discussion, without which an entirely theoretical model such as that produced is perhaps an unreliable basis upon which to formulate policy prescriptions.

There is also a recent paper commenting on cross-immunity (https://www.nature.com/arti... "https://www.nature.com/articles/s41577-020-00460-4)") the conclusions of which the authors should carefully consider.

On 2020-10-16 12:16:12, user Torbjørn Wisløff wrote:

I would seriously consider revisiting the analyses. In Figure 2c, the RR of 1.00 seems not to correspond with the somewhat diverging curves. In Figure 2a, on the other hand, the RR is 0.95, but the curves follow each other very closely.

On 2020-10-17 15:54:52, user Dan Myers wrote:

The results make sense, actually. Antivirals are usually not as useful unless used early.

On 2020-10-18 07:14:28, user Robert Clark wrote:

A key comparison was left out of the report, the effect of HCQ on patients specifically under invasive mechanical ventilation. This is a key category beyond just being “ventilated”. This is when the lung inflammation is so severe the patients have to be intubated, i.e., given a breathing tube inserted down the throat.

HCQ is a highly effective anti-inflammatory. Then it would be this case where it would be most effective for hospitalized patients. Note that the study was primarily focused on these drugs anti-viral effects. HCQ is the only one of them that also has an anti-inflammatory effect. Indeed it may be the only drug among all those being considered for COVID-19 that has both characteristics.

Note that in the RECOVERY trial it was specifically for THIS type of ventilation that the steroid dexamethasone was found to cut mortality, via its anti-inflammatory effect.

But in Table S1 of the Supplementary file to this SOLIDARITY report it states the “ventilation” discussed includes both invasive and non-invasive types:

Table S1

The authors need to add to the report the effect of HCQ specifically for patients under invasive mechanical ventilation.

BTW, the report does show the result of Remdesivir on invasive mechanical ventilation patients in Fig. 3 of the report itself, showing null effect. But this was not a useful set of data anyway because Remdesivir does not have an anti-inflammatory effect.

The more important and relevant case of HCQ was not shown.

Robert Clark

On 2020-10-16 12:17:32, user Criticical Opinion wrote:

Unfortunately it is not clear if there was a difference between a diagnosis of OSA vs treatment for OSA, severity or degree of OSA, or type of treatment for OSA. Without this information, the utility of these findings is questionable at best.

On 2020-10-18 04:17:11, user C Jones wrote:

My family has used the self-administered oral swab at an LA City Covid testing site each time we've tested.<br /> My son (19 yrs) tested last Friday 10/09 & received Negative result.<br /> He was out late on Saturday & I had him retest on Monday 10/12, and he received a Positive result.<br /> He tested again yesterday 10/16 and received a Negative result.<br /> His father & I both tested on 10/09, 10/14, and 10/16 - all results Negative.<br /> Very confusing. How do we proceed?

On 2020-10-20 17:57:35, user Dinofelis wrote:

It is strange to conclude that one observes statistically significant "<br /> PCR negativity in intervention and control groups were (day 7, 182 (52.1%) vs. 54 (35.7%) (P value = 0.001)"and concludes that there is no effect.

Let us remember that statistical non-significance of rejection of H0 is not equivalent to proof of absence of effect. It simply means that the test didn't have enough power to prove anything.

In order to prove absence of effect, one needs to reject with statistical significance the hypothesis that the effect is larger than a given threshold.

I have seen many papers confusing "statistical absence of significance" with "proof of H0".

On 2020-10-31 12:07:38, user Scandinavian Journal wrote:

One issue not talked about much is that a normal HCQ dose as per on the package is not considered lethal, has been around for 50 years with good reliability and costs a few dollars for a package. If I caught the virus would I decide that this is useless because some study say so while others say it is effective ? I would of course put myself under this treatment.<br /> If it is useless well what damage did it do. If it was effective it may have saved my life or made the disease progression milder. For a few dollars. Easy choice.

On 2020-10-21 14:08:08, user Darren Brown; HIV Physiotherap wrote:

The EUROQoL EQ-5D-5L self-reported health related quality of life (HRQOL) measurement tool has been used for statistical purposes, however this baseline data of EQ-5D-5L scores across 5 domains (health status) and index value are not reported. This would be useful data to understand the HRQoL of the sample, with respect to population normative data (https://euroqol.org/eq-5d-i... "https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/population-norms/)").

On 2020-10-21 19:18:46, user Alan Wilson wrote:

I would recommend the authors to check other recently published ASRM abstracts in the topic:

https://www.fertstert.org/a...

https://www.fertstert.org/a...

On 2020-10-23 11:44:52, user Alexander Samuel wrote:

Dear authors,

I fully agree with your introduction, discussion, and everything is done correctly in this paper. After scientific misconducts from Gautret et al. in Didier Raoult's IHU Marseille, and its transfer to USA through J. Todaro followed by Zev Zelenko's strange comments, there is clearly a situation that went out of control about hydroxychloroquine.

My comment on your work is that Recovery + Solidarity weight almost for 90% of the results, In a meta-analysis, I expect a significant effect of all (or most) studies, here it seems like the results are a new read of Recovery + Solidary, with comments on very low weighted unpublished or published clinical trials. Of course, authors mention that there is still no effect in the absence of Recovery, indirectly (published vs unpublished, high dose vs low dose). I think it would be important to not just make a "second read of recovery data" (exagerated statement, sorry for the way it is said). The discussion on the difference between high / low dose is what interested me most in your paper, and would be worth more comments or even analysis.

I would suggest more theoretical molecular biology bibliography (molecular effects of HCQ might reduce the immune reaction more than affect viral cell entry), more introduction elements on in vitro data (which clearly did not favor HCQ that much) for the next effort mentioned in this paper !

Anyways, this is a good paper since it is very honest and shows data properly, congratulations for this work.

Best regards.

On 2020-10-26 00:13:04, user Mahdi Rezaei wrote:

The accepted and peer-reviewed version of the article is now available via open-access Journal of Applied Sciences. DOI: https://doi.org/10.3390/app...

On 2020-10-27 03:12:46, user Critical Dissection 2 wrote:

Dear authors,

First I just want to say I think that it was great you pursued such an important topic. There were a lot of good things about your article like your clear abstract that very well laid out the different parts of the paper and the main summary of each section. I also like that you laid out the limitations of your study and how they should be solved for in further investigation of this topic. However, there is still some room for improvement in this paper. I thought that the introduction could use more background to contextualize the issue and put some scope into it to explain why people should expand upon your results and see if the data is helpful in the future. I also think that the figures need more explanation in the results section, unless a highly experienced physician is reading it, it is a little hard to tell what we are supposed to be looking at and drawing from the figure that supports your hypothesis. There was also an emphasis drawn between the two patients whose ablation was done with a little more targeting of certain factors compared to patients who underwent standard ablations that was only mentioned in the discussion but is a great point that I think should be brought up earlier maybe somewhere in the results section. I think with these changes you will have a good paper.

On 2020-10-27 16:42:24, user Kamran Kadkhoda wrote:

Again no good panel of confirmed common CoVs to exclude cross-reactivity especially to an Ab like IgM with poor affinity maturation.

On 2020-10-28 08:35:30, user Rasmus Skov Husted wrote:

The final peer reviewed article is published open access - please follow this link: https://bit.ly/3mrLlvZ

On 2020-10-28 16:35:38, user Edsard wrote:

I think we have a chicken and egg issue here. Your pollen theory is pretty good but also the reason why scientist always say: Correlation is not causation. Your pollen is the result of the weather (temperature and humidity, which has explained seasonality of the flu for 10 years already). Here is our paper. https://www.medrxiv.org/con...

On 2020-10-28 17:53:21, user Sam Wheeler wrote:

It it so that non-vaccinated hospital personnel are forced to wear a mask almost all the time to prevent flu, so the protection of flu vaccine is even greater than this study tells?

On 2020-10-30 17:10:52, user gatwood wrote:

I suspect there could be a strong corellation between vaccination status and following a strict adherence to all COVID anti-infection guidelines, PPE etc... Experienced and medically trained Drs and nurses more likely have been vaccinated and also are more likely to follow PPE wearing and careful anti-infection routines. Support staff (food service, assistants and claening staff) with less formal medical training and understanding of infection are probably less likely to be vacinnated and also may be less likely to carefully employ all technical anti-infection measures. Would this account for the vaccinated folks having less COVID infection?

On 2020-10-22 11:33:50, user Paul Peerbooms wrote:

It would be interesting to see the protective effect of the flu-vaccination when only staff with contacts with patients is considered.

On 2020-10-29 15:18:11, user bljog wrote:

In the results you mention "A cluster of sequences in clade 20A has an ad- ditional mutation S:A222V colored in blue" but the Figure 1 has an annotation in blue for S477N.

On 2020-11-03 23:04:46, user David Markun wrote:

Nice study. Copy edit: " If we instead look after September 1, 2002" should refer to 2020.

On 2020-11-04 14:41:47, user Rodrigo Quiroga wrote:

Are´t these results expected regardless of children´s proneness to infection and infectivity? Up until August, the time periods with open schools were also periods with low viral propagation in the UK.

Wouldn´t the interesting period to observe with such an analysis be precisely August-November, with open schools and increasing case numbers?

On 2020-11-05 12:26:14, user Sandra Chydé wrote:

Dear authors,

You are citing one of my papers (reference 15) in a misleading way here : " There are concerns that the use of e-cigarettes in never-smokers may increase the probability that they will try combustible tobacco cigarettes and go on to become regular smokers, particularly among youth and young adults [13-15].".

First, our methodology focused ONLY on ever-smokers aged 17 having experimented with e-cigarette.

Second, we found that in this population of 17 yo, among ever-smokers, those who declared having ever used e-cigarettes were LESS likely than those who did not to transition to daily smoking at 17: RR =0.62 95 %CI [0.60 – 0.64].

This analysis is strongly robust and relies on a sample of 21,401 respondents.

Best,

Sandra Chyderiotis, Pharm.D, MPH

On 2020-11-07 10:32:09, user Ivan Ivanov wrote:

They will never share the primer sequences as the test is being commercialized already. The idea is interesting however I cannot imagine the price for 500ul LAMP reaction. Also what's the point to put DNase and carrier DNA together in the mastermix.

On 2020-11-14 02:00:10, user Melimelo wrote:

Hi, very interesting article. Which software did you use for initial qualitative coding and subsequent text mining? are there particular commands or functions in a given software package that were useful? are you sharing your code anywhere (eg github?)

On 2020-11-14 04:23:20, user Rich Kibbee wrote:

Error, this pdf lists a Centricon 70 Plus 100 kDa filter ...the first version lists it at 10 kDa.

On 2020-11-15 21:48:01, user Ands Hofs wrote:

We in germany do re-testing of positives on a regular basis, and the result is that false-positive diagnostic findings that are actually filed to the patient are in the range of 0,001 %. Even if testing activity of healthy subject was high up to September, the number of people that had a wrong test result is something like a handful a week and totally acceptable in the face of the alternative. Especially since one does a second test some days later.

But right now we have positive testing of 25% of samples in Frankfurt (Main),e.g., just mentioning this to get the perspective right, water is rising above neck to the lips...

A few people (like 1-5%) mentally infect 30% insecure anxious people here, damaging our wakefulness to keep our viruses for ourselves, prohibiting smart distancing to be practice in private contexts, behind closed doors in companies and among friends and neighbors all the same, and this is making the 2nd lockdown necessary.

And causing thousands of deaths not necessary when they would obey the democratic decision: we do not want to do triage. We want to keep the numbers low. We want to keep our viruses to ourselves. We do not want to have unnecessary lockdowns burning away existences, jobs, money... But what choices do we have?

Since we wasted the summer where we had the chance to get incidence real low.

Now the only thing that can save our neck is a (pre-) test that is really free for everyone, and MIT has one: https://digitalreality.ieee...

Every one writing about false positives should weigh his words thoroughly.<br /> Not the rate of one single test method is what people want to know. <br /> They want to have approved quality testing and numbers for "their" lab.

These numbers are there in every German lab, since they are obliged to certify every test they offer and to take part in Ring Tests where labs and their certified tests are tested. This is done by sending a lab unknown but specially prepared samples that each lab has to let run through the lab on a regular basis. This also is done to get quantitative tests to comparable levels between labs.

Comparable Levels for Covid-19-Infected patients:<br /> It is a pity that we do not let some piece of human DNA normally found from throat swabs run together with the Sars-CoV2 Test on a regular basis, resulting in viral units per human DNA count, because this would enable us to estimate the viral load at the place where the sample was taken. It would outrun many variabilities that occur when taking samples that affect the amount of material gained in the sampling process, and one could monitor viral loads across the time line for each infection with high therapeutic value. <br /> I'm so curious if someone has done this with the gargling method for probing, since here the local variability in infection density is not playing any role any more, as is the case for the question how infectious one could be in a certain state of the infection.

Boston children hospital has done this in their study on viral loads in children, where for the first time it was found that children, regardless if having symptoms or not, have viral loads like heavily ill adults. <br /> Since their lungs are smaller proportional to their age and development, of course the net amount of aerosols produced by a small child e.g. up to 8 or 10 years is smaller ( - but proportional to the loudness of their voices ;)) <br /> Still - starting with 11 or 12 years, it starts to reach adult levels, meaning we must do DIY patchwork air ventilation with heat recovery mechanisms out of vapor barrier film and 2 vents in schools or let the pupils sit in the cold of fresh air or 8hrs / day under some masks that muffle sound (many innovative ideas for DIY masks are asked here for).<br /> I like the nordic approach either to do home schooling or do classes under the trees for the younger ones, leaving a lot of space in the school for elderly pupils, especially in classes wanting to have their final exams ;)

Andi

On 2020-11-26 14:59:36, user Raul_Y wrote:

This paper is of ridiculously low quality. They even forgot to mention what dose of dutasteride was used.

On 2021-03-24 14:52:53, user Pedro Negreiros wrote:

The paper is of good quality. The results need , however, be reproduced by other researchers.

On 2021-06-09 23:47:14, user Gnash wrote:

Is there any data on any group who received only AZM or only received HCQ?

On 2021-06-10 09:22:10, user cat's eyes wrote:

What were the baseline characteristics of the 37 patients who survived on HCQ compared to the patients who died? From Table 1 patients who survived were generally healthier and younger than those who died. Table 4 should provide adjusted and unadjusted hazard ratios. Also, did you test for interactions between HCQ/AZM and predictors such as age and steroid use?

On 2020-08-03 14:07:24, user Monica Sidén wrote:

I am a nurseryschool teacher in Sweden. My bloodgroup is AB+.<br /> As I can understand it is a rare bloodgroup and I can receive blood from any other bloodgroup( since I don´t have any antivirus against any other bloodgroup). Now I am very wooried that I am likely to be at a high risk. I would be very pleased if someone can explain.

On 2020-08-05 08:11:07, user Rosemary TATE wrote:

I have performed a review of this paper on publons https://publons.com/review/...

On 2020-08-09 17:02:50, user David Leidner wrote:

Article says all data fully available, but no link to the data is provided.

On 2020-08-09 21:12:25, user Cynac wrote:

The results appear to show a significant relationship between menopause and diagnosis of Covid-19 by your algorithm. There is no significant association with positive Covid test ("proven" Covid) or severe disease.<br /> The significant symptom associations do include fever, but not cough or even the anosmia. Whereas "skipping meals" is a highly significant association.<br /> This brings the major possibility that it is your algorithm for diagnosing the disease that best relates to menopause, perhaps by some quirky inclusions.<br /> There must also be some difficulties in allowing for age etc. When the influences of these factors themselves are not precisely defined.<br /> This study is clearly worthwhile, and of interest. But the way the abstract will be viewed in the media might be an over-simplification.

On 2020-08-13 00:49:59, user Jesse Baker wrote:

Regarding a passage in this MedRxiv post (July 21, paragraph 3 with citation to reference #15), “Additionally, recent clusters of COVID-19 cases linked to a…restaurant in Wuhan are suggestive of airborne transmission.”

Although the index case having lunch on Jan. 24 was from Wuhan, the restaurant was in Guangzhou. Indeed, its location far from Wuhan so early in the spread of Covid increased Guangzhou CDC’s confidence that the other patrons were infected by the index case and no one else.

On 2020-08-13 07:57:05, user Zeit wrote:

Very interesting manuscript. I think it may be wise to remove isotopes from your data as it seems clear that you have associations of monoisotopic peaks and their isotopic peaks with phenotypes. If you correlate the retention times of ions most correlated with each other by area count/signal, it should reveal that they are non-independent ions.

On 2020-08-13 20:06:24, user Rhyothemis wrote:

Could the low number of deaths in Kenya be at least partly attributable to low per capita protein consumption? It seems as though many countries with low per capita protein consumption rates are reporting relatively low per capita COVID death rates. Mechanistically, such an association (if it exists) could be related to lower baseline mTOR activation.

On 2020-08-15 23:30:43, user Nan wrote:

To those who tweeted and regarded this as evidence that masks don't work,

This article does NOT imply masks don't work. If one wishes to draw such a conclusion, a direct comparison is required on the disease risk when wearing masks versus not. From both the fifth and sixth comparison in the figure and a related article (https://www.bmj.com/content... "https://www.bmj.com/content/369/bmj.m1442)"), masks are better than not wearing at all! This article only says physical distancing is very important for cloth and surgical masks. It means that besides wearing normal masks, I should be cautious about a strict physical distancing. This agrees with common sense that the more protections (e.g., masks, distancing, etc.) we have, the safer we are.

Also, is physical distancing always easy and tangible to follow? The answer is no. You cannot guarantee that you are always in safe distances with other people in the street. In contrast, masks are a lot more perceptible. They reduce exposure to the contaminated air. Masks are also a sign of caution. A sign that everyone should protect their community by reducing transmission.

On 2020-08-16 19:15:38, user Skadu SkaduWee wrote:

One of the fundamental assumptions of the paper is the use of a previously tested positive saliva sample to prepare the serial dilutions used for the limit of detection studies. However, the authors omit to declare how this initial copies/ul value was arrived at and by whom.

On 2020-08-17 09:34:02, user buddinggenetics wrote:

The principal author has stated in media that the cost per test is $10, however in the text the cost is listed as $1.29-4.37/sample. Pricing should be consistently stated to avoid misleading the public and/or scientific community. Also, the text states that the price per sample is low, which is a relative term, and gives no price estimates of other established tests for comparison.

Multiplexing the samples is a fundamental improvement of testing, however there is insufficient evidence to show eliminating the N2 primer set is justified. There needs to be an analysis of how many inconclusive test results (N1 positive and N2 negative/ N1 negative and N2 positive) would now become positive or negative tests as a result of eliminating the N2 primer set. Also, in Supp Fig 2, the data appear irregular with a bimodal distribution when a Gaussian distribution would be expected. The authors do not discuss the reason for this in the text. Furthermore, the failure of the N2, E, and ORF1 sets may be due to the HEX fluorophore. Would they have worked using a different fluorophore? Would the authors have eliminated N1 if they had by chance used HEX on N1?

The Source Data files are not posted.

On 2020-08-17 18:18:04, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-08-18 07:49:11, user Rawi Naddaf wrote:

This research has been published in Experimental Biology and Medicine. DOI: 10.1177/1535370220941819<br /> Please cite this publication when when you reference this work.

On 2020-08-18 12:42:45, user Saban Öztürk wrote:

The peer-reviewed and published version of this article can be accessed using the following DOI:<br /> https://doi.org/10.1002/ima...

Also, this link can be used for direct access to this article: <br /> https://onlinelibrary.wiley...

On 2020-08-18 18:07:44, user Eric Vallabh Minikel wrote:

Excellent, important study, with carefully considered conclusions from the authors. Some readers may assume that if plasma NfL can become elevated 2y before onset, then NfL could be used as a prevention trial entry criterion, a primary endpoint, or a basis for deciding which patients are eligible for drug access/reimbursement. Importantly, the authors of this paper do not assert that their data support those applications. I believe there are three key considerations here that should be factored into any clinical application of plasma NfL quantification in pre-symptomatic genetic prion disease: genotype (rapid vs. slow PRNP mutations), age (affects reference range for NfL), and cross-sectional (as opposed to longitudinal) number of people in a prodromal state at any given time. I have written a detailed blog post here: http://www.cureffi.org/2020...

On 2020-08-18 20:34:55, user Lauren Call wrote:

I found this study through a link in a CNN article, along with the quote: “Gommerman said since scientists have not seen a record of re-infection, even with as widespread as the pandemic is, that strongly suggests the body's immune system is working well against this threat, and re-infection is less likely.” I am surprised they haven’t “seen” a re-infection, because I’ve had 2 positive COVID-19 tests, separated by 3 months, with a negative antibody test in between. Both times I had classic coronavirus symptoms, but they were distinctly different cases.

On 2020-08-19 11:00:05, user AbsurdIdea wrote:

Have I understood this right: " Vitamin D dose was not significantly associated with testing positive for COVID-19."? So taking vitamin D does NOT reduce the probability of testing positive for CoViD-19...Then, why take it against CoViD-19? For the rest - correlation or causation? Healthier people are likely to have a higher probability of sufficient vitamin D, conversely, people in poor health for any reason are likelier to have low vitamin D. Also there is a difference between becoming infected i.e. the virus actually entering into a person and propagating and the degree of illness and complication once being infected. This study does not appear to address these factors. Finally the phrase "499 had a vitamin D level in the year before testing" does not make sense. All people have some level of vitamin D.

On 2020-08-19 17:59:56, user petsRawesome1 . wrote:

"Of the 43 patients randomized to ConvP 6 (14%) had died while 11 of the 43 (26%) <br /> control patients had died."

That sounds like the study showed promise on the key metric, mortality, it just did not have enough data when it was stopped. It would be good to be very clear about the reasons for discontinuing the study, as the New York Times of Aug 19, 2020 is quoting this paper as "Last month, one such trial in the Netherlands was stopped when researchers realized that patients given plasma showed no difference in mortality"

On 2020-08-20 01:45:02, user giorgio capitani wrote:

How it can be proved without any doubt that the virus present in the aerosol actually infects a person? it can present but be harmless. Where is the evidence of the actual trasmission of the infection? the presence in the aerosol is not evidence of the transmission of the virus it's another pair of shoes. Or somebody can be infected and others not. How can you tell one thing from the other? they are two different moments: the presence of the virus in the aerosol, the actual transmission of the virus.

On 2020-08-21 13:38:24, user Susan Levenstein wrote:

If I understand the paper correctly, its most striking result is the isolation of Patient 1's virus from the VIVAS air sampler located 4.8 m away. But according to the Figure, Patient 1 had to walk right past that air sampler, closer than 1 m, every time he went to the bathroom. Couldn't that be a simpler explanation for how it picked up his virus?

On 2020-08-23 16:11:32, user Ang wrote:

Hello there,<br /> below a question for someone with the right competence.

True the approach of this work is great, it might result that they are right or wrong we'll see, starting from the review outcomes. However a common person would ask: "Why can't we do a direct and conclusive experiment about transmissivity through aerosol?". A direct experiment is to put a never infected person in the same room with a SARS-CoV-2 ill person, without the physical possibility to exchange any particle between them except air/aerosol. 100 person would cover a good statistics in terms of age, gender, time of exposure and other characteristics of the volunteer. Is this possible? How can be that in the entire world we cannot find 100 voluntaries that are available for the following experiment. Why is this something not done yet?

On 2020-08-22 07:54:04, user Drifa Belhadi wrote:

Updated preprint available here : https://www.medrxiv.org/con.... Published in Clinical Microbiology and Infection, doi: 10.1016/j.cmi.2020.05.019

On 2020-08-24 04:45:43, user Bill Pilacinski wrote:

Now it will be important to identify those in the population who are immune so that the early limited supply of vaccine can be used for those susceptible individuals of high priority as we attempt to reach herd immunity.

On 2020-08-24 06:26:34, user Stan Himes wrote:

For COVID-19 you should include co-morbidity data, without this key information (which may be contained in full article) the data presented is worthless.

On 2020-08-24 15:34:57, user Eva Lendaro wrote:

Hello,<br /> I question regarding what does the vector beta account for. it supposedly includes policy dummies of businesses, restaurants, movie theaters, and gyms being allowed to reopen but in practice it is not very clear how these are accounted for. Is the capacity at which they were allowed to reopen considered? are the categories considered separately?

I would also like to point out this systematic review on this exact topic published on may 26th, 2020 on bmj that is nowhere mentioned in this article but would be rather important to include for completness.

https://www.bmj.com/content...

Best Regards,<br /> Eva

On 2020-08-25 21:29:56, user Chris Raberts wrote:

I am not sure how the authors can use a study that speaks of N95 and 12-16 layered cloth masks and come to a conclusion like this. (reference 31).

In a recent comment (https://www.thelancet.com/j... "https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30352-0/fulltext)") the same authors speak of a range of 6% to 80% of mask benefits regarding reduction in transmission. I wonder what percentage was used in this paper, but given the results I'd assume it is on the higher end. Also that paper does not speak to schools, mostly to health care settings.

More transparency would be great but overall this paper looks like agenda and not science :/.

On 2020-08-27 11:22:55, user pto wrote:

I thought the index cases of that conference were all local residents of the Boston area. If so, that certainly wouldn't rule out a previous introduction a few weeks earlier. Say when international university students returned to Boston 3 to 4 weeks earlier.

On 2020-08-27 13:31:15, user Joe Psotka wrote:

Using data from Florida creates misleading expectations because Florida's decrease in March and April was largely from Snowbirds' and part time residents' departure from the State. Some people estimate that one-third of Florida's winter population leaves in the Spring to avoid the summer heat.

On 2020-08-27 23:26:12, user Vinci P, MD wrote:

There might be other explanations for better prognosis in post-menopausal women taking oestradiol: they were probably healthier than women not taking oestradiol, because HRT improves health. <br /> In addition I cannot understand why all post-menopausal women have better prognosis than men, since their estrogens are similar to those of men. Maybe it is the absence of testosterone, and not the presence of oestradiol, which makes the difference.<br /> Could you comment this, please?

On 2021-06-30 12:12:11, user SinoBen wrote:

The language used here is seething with something I've not commonly found in the typical scientific paper.

On 2020-08-29 21:03:28, user Sam Well wrote:

I cannot believe your work is being "advertised" as a hail mary when you conclude..."Additional results from larger randomised controlled trials are needed"

Please refer to the "sale" of your source:<br /> https://www.eurekalert.org/...<br /> https://www.dailymail.co.uk...

On 2020-08-30 15:05:05, user Henry Johnson wrote:

Does anyone know whether similar experiments have been done with woodwind instruments. I'm particularly interested in clarinet. The instruments work differently. The sound comes out of a variety of places...

On 2020-09-04 19:44:32, user Art Framer wrote:

Excuse my ignorance but it seems that the tests for covid 19 are looking for the virus itself. Wouldn't the tests have a higher rate of success if they looked for signs of the body's reaction to the virus?

On 2020-09-07 16:03:04, user Joe B wrote:

We don't know how long ago the vitamin D levels were obtained in these patients. This is especially true in the COVID patients, because we have no idea if they truly were "deficient" at the time of their infection. Additionally, you never tell us in the methods that you were going to examine supplementation, and how you were going to do that (and assure adherence). Can vitamin supplements not be purchased over the counter in the countries involved in this study? Finally, I assume you categorized people by "sex" and not "gender" as sex if the term used for male/female DNA based differences.

On 2020-09-07 16:34:55, user A. Baluja wrote:

Hi! The last version for the reference [Baluja et al, 2020] has been finally peer-reviewed and published in:<br /> https://www.tandfonline.com...

The updated version of the preprint:<br /> https://www.medrxiv.org/con...<br /> Thank you for continuing research in such important topic!

On 2020-09-09 19:12:11, user Michael Bishop wrote:

I don't believe the authors' data, which would imply that SARSCOV2 was circulating with little increase or decrease in Dec 2019 - Feb 2020 until suddenly taking off in late Feb early March.