On 2020-04-16 23:50:15, user Brian T wrote:

MISLEADING UV DOSE! DO NOT FOLLOW THIS 5 uW/cm2 dosing!

The UVA/B meter used in this study “General UVAB 137 digital light meter (General Tools and Instruments New York, NY)” (look up General Tools UV513AB Digital UVA/UVB Meter on Amazon) advertises measurement from 280-400nm, barely overlapping the wavelengths used in this study, 260 – 285 nm. Furthermore, this meter does not report on the energy at a given wavelength. Its possible that this study is grossly underreporting the dose of UV needed because their meter doesn’t read many of the wavelegths used.

Previous research on SARS and MERS used 254 nm and noted much, much higher energy needed to kill these corona viruses vs this study's reported 5 uW/cm2):

• MERS is inactivated at 90 uW/cm2 x 60 mins[1], dose of 0.324 J/cm2<br /> • SARS is inactivated at 4016 uW/cm2 x 6 min[2] , dose of 1.446 J/cm2

[1] http://www.diniesturkiye.co...

[2] https://www.sciencedirect.c...

On 2020-04-17 20:25:02, user Mortal Wombat wrote:

Hold on, they made no adjustment for self-selection of symptomatic people in their study?

Researchers, at least please tell us the number of people shown the Facebook ad so we can have some sense of the potential for self-selection -- i.e. how many saw it but chose not to participate.

This seems problematic given the population demographic adjustments that were necessary. The researchers say that white women were heavily oversampled while hispanics and Asians were heavily undersampled, and that population adjustments led them to adjust the observed prevalence of 1.5% up to a population-weighted prevalence of 2.81%.

It would appear highly likely that the relatively affluent population (white women) would have the interest and capacity to be a roughly random sampling -- people with no prior symptoms just interested in knowing. Whereas for lower-income populations, there may be less ability to simply participate out of interest, and may have been a higher self-selection drive of the previously symptomatic to get themselves tested.

Thus I find the upward adjustment in the numbers quite suspect. To come up with an overall result that's _higher_ than the raw outcome of the study when you know that people who've been sick will be the most motivated to get themselves tested just seems perverse.

Did the study not even ask people whether they had been sick over the past couple months? Why not? That at least could've given some sense of whether self-selection was biasing results in the samples.

On 2020-04-18 02:01:37, user mendel wrote:

First, he picked the county that had the earliest cases in California and had the outbreak the first, ensuring that the population would be undertested. This means that it's likely that every other county in California has fewer unregistered infections than Santa Clara.

Second, study participants were people who responded to a facebook ad. This is a self-selected sample, and this property completely kills the usefulness of the study all by itself. This is a beginner's error! People who think they had Covid-19 and didn't get tested or know someone who did are much more likely to respond to such an ad than people who did not. (By comparison, the Gangelt study contacted 600 carefully chosen households per mail, and 400 responded. Still somewhat self-selected, but not as badly.)

Third, age is the one most common predictor of mortality. He did not weigh the results by age, and old people are underrepresented in the study. Anything he says about mortality is completely useless if we don't know how prevalent the infection was in the older population. (In Germany, cases show that the prevalence among tested older people was low initially and took a few weeks to rise.)

Fourth, instead he weighs prevalence by zip code--why? This exacerbates statistical variations, since there were only 50 positive results, and Santa Clara has ~60 zip codes. If you have a positive result fall on a populous zip code by chance where only a few participants participated, then the numbers are skewed up. They must have seen this happen because their estimated prevalence is almost twice as high as the raw prevalence.

Fifth, the specificity of the test is "99.5% (95 CI 98.3-99.9%)". This means that theoretically, if the specificity was 98.5%, all of the 50 positive results could be false positives, and nobody in the sample would have had any Covid-19. This means the result is not statistically significant even if the sample had been well chosen (which it wasn't). (It's not even significant at the 90% level.)

Sixth, they used a notoriously inaccurate "lateral flow assay" instead of an ELISA test and did not validate their positive samples (only 50) with a more sensitive test -- why not?

Seventh, The Covid-19-antibody test can create false positives if it cross-reacts with other human coronavirus antibodies, i.e. if you test the samples of people who had a cold, your speficity will suffer. Therefore, a manufacturer could a) test blood donor samples, they not allowed to give blood if they have been sick shortly before; b) test samples taken in the summer when people are less likely to have colds than in March.

To state the previous three points this in another way, a large number of positive results (a third if the specificy is actually 99.5%, but probably more than that) are fake, and depending on which zip codes they randomly fall in, they could considerably skew the results.

On 2020-04-18 06:35:20, user DomesticEnemy wrote:

About 5 weeks ago based on a study of the Diamond Princess cruise ship I estimated the death rate to be around 0.23% or so. Welcome.

On 2020-04-18 18:39:18, user jj wrote:

Where is the discussion of selection bias? You invite folks to get tested by advertising on Facebook... I think there will be an over-representation of folks who fear they have COVID-19 based on their recent interactions in places with or around COVID-19 cases.

Without randomization to eliminate self-selection bias, the authors should not be making any far-reaching conclusions that are now being picked up and reported by the media without providing proper interpretation.

I think this publication should be rejected for not doing this study properly.. and then seeking publicity!

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 2020-04-22 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 2020-04-23 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 2020-06-24 05:32:27, user Gavin Donaldson wrote:

Were patients ineligible to the dexamethasone arm excluded completely from the recovery trial or allocated to the other arms including the standard care arm?<br /> Could the reasons for the exclusions be included in a consort diagram of the participant flow.<br /> There was an imbalance between the two arms in age. Is there any data on obesity or hypertension since these are important risk factors for covid-19 mortality.

On 2020-06-29 17:14:09, user Aiman Tulaimat wrote:

The study reports a mortality ~40% in patients on vent on the control arm. This is much lower than what is reported by the critical care audit from the UK, which reports mortality > 60% in such patients. The study reports ~25% mortality and ~60% discharge alive. Are we missing 15% of patients? If the analysis is a Cox hazard, why is the report using relative risk? How did 13% of patients with no oxygen therapy die? This is very high? Did their covid deteriorate or did they die from other reason? why were these patients hospitalized if they were not hypoxic? did they decline life support when it became needed? where they not on oxygen because they were in hospice like setting? how did the other patients die? Were patients on the ventilator made DNR early? Was prone position used? Was it used more in the dexa arm? Was there imbalance in the ramdomization by center?

On 2020-07-10 18:25:07, user Joanna Spencer-Segal wrote:

The authors speculate in the discussion that "It is also possible there is an effect via mineralocorticoid receptor binding in the context of SARS-CoV-2 induced dysregulation of the renin-angiotensin system." It is not clear what this means, but dexamethasone has minimal activity at the mineralocorticoid receptor, which distinguishes it from the other corticosteroids often used in critically ill patients (methylprednisolone, hydrocortisone). More clarification of what they mean regarding "mineralocorticoid effect" and rationale about why dexamethasone was chosen for this study would be welcome.

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 2020-07-02 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 2020-07-05 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 2020-07-14 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 2020-07-16 10:16:25, user Hagai Perets wrote:

See https://arxiv.org/abs/2007.... for a possible explanation of the observed differences and origin of herd immunity, from a preceding low-virulence strain.

On 2020-07-18 18:45:51, user James Truscott wrote:

Hi, I think there is an error in the model as laid out in Supplementary Text File 1. The variable z represents all non-susceptibles, which includes the infectious-infected , y. The rate of loss of immunity term in equation 1 is gamma*z, but infectious individuals presumably don't have immunity to lose. They first recover (at rate sigma) and then can lose immunity. The term in equation 1 should therefore be gamma*(z-y). This change will affect the algebraic result, probably, and may change the dynamics significantly at some time points and/or parameter values.

On 2020-07-21 19:08:09, user Jeremy Rolls wrote:

Fascinating paper. Looking at the antibody data (such as there is any published here in the UK) about 18% of people in London have antibodies compared to about 8% nationally. On that basis alone 82% of Londoners may still get infected compared to 92% nationally - i.e. you would expect the mortality rate in London still to be pretty close to the national rate. Yet the hospital death stats for covid-19 in recent weeks shows London's rate consistently to be less than 40% of the national rate. Something else must, therefore, be going on - a) London is locking down better (unlikely), b) antibody immunity does not give the complete picture (possible given the data coming out of Sweden showing that for every person having antibodies two others have T-cell immunity) or c) there is a % of the population who have pre-existing resistance (from exposure to other corona-viruses) or are biologically incapable of getting infected. Ruling out a), a quick bit of maths shows about 75% of the population must fall into b) or c). So, on that basis, in London well over 90% have either been exposed to the virus or have pre-existing immunity and maybe 80-85% nationally. I suggest herd immunity has probably been achieved in London and is close in many other parts of the UK.

On 2020-07-17 01:54:28, user Born in Akron wrote:

Is LD-RT a widely known specific therapy? This paper does not indicate the type of radiation. X-rays, Gamma rays, proton accelerator, sun lamp? The dose is 1.5 Grays = 1.5 Joules/kg = 150 rad. But the biological effect in rem or Sieverts depends on the type of radiation and duration of the exposure. Even if LD-RT is always, say, X-rays, shouldn't the effects depend on the energy of the X-rays? Unless LD-RT has a unique definition this preprint is deliberately irreproducible, perhaps to gain advantage for patent protection during a worldwide pandemic.

On 2020-07-20 21:52:28, user Deborah Barr wrote:

It might be useful to correlate by medications taken. Depletion of magnesium and zinc affect clotting.

"drug-induced nutrient depletions are well known by pharmacists, many are underdiscussed and subsequently underdiagnosed and undertreated."<br /> 33 citations.<br /> https://www.uspharmacist.co...

Uwe Gröber's Magnesium and Drugs, https://www.ncbi.nlm.nih.go... with an excellent image of ways that drug interfere with nutrient levels in the body, and a table specific to Magnesium.

On 2020-07-21 15:50:10, user OxImmuno Literature Initiative wrote:

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

On 2020-07-21 16:14:03, user Kamran Kadkhoda wrote:

Baes on the current estimates, the sero-prevalence in Idaho is around 4% at most; such high percentages are most likely false positives; I refer authors to the study just posted here on medrxiv from China showing sero-prevalence of 2% or less in Wuhan! They used PRNT to confirm the results. That's the right way. <br /> Abbott is clear in their IFU by saying they did NOT use samples from cases with confirmed infection with common CoVs…<br /> Despite publications using "convenience samples" specificity shows its shortcoming while used large scale in the field...here's one example!

On 2020-07-22 17:00:56, user Robin Whittle wrote:

As Karl Pfleger suggested, I hope there will be more detailed information on 25OHD levels, symptoms at admission and as treatment progresses.

In light of a recent review (Charoenngam & Holick for a recent review https://doi.org/10.3390/nu1... "https://doi.org/10.3390/nu12072097)") which states that 40 to 60ng/ml 25OHD is required for proper immune system function, the 25OHD thresholds and D3 doses seem inadequate. This article also recommends an initial 12.5mg D3 (50,000IU) for all COVID-19 patients.

According to the present article, patients with 30ng/ml or more are given no D3 at all. Daily doses for those with lower levels are only 0.02mg (800IU) per day, which is a 20% or less of what most people would require to maintain 40ng/ml - assuming the supplement was taken with a fatty meal and well absorbed. https://journals.plos.org/p... indicates that average weight people need about 0.125mg (5000IU) a day to reach the middle of the 40 to 60ng/ml target range.

Surely all these low 25OHD levels (and the researchers report 21.6% of patients with initial levels below 6ng/ml and some below the 3.2ng/ml detection limit) warrant urgent action. What objection would there be to bringing all patients up to at least 40ng/ml with oral or IV 25OHD cholecalciferol (Rayaldee)? This would go into circulation immediately without relying on potentially hepatic conversion of D3 to 25OHD, which takes days or a week or so - even if the liver is functioning properly.<br /> The present article cites, as prior observations of low vitamin D levels correlating with COVID-19 symptom severity, an Indonesian article (26), an Indian article (27) and one from the Philippines (28). The first two have been withdrawn. Please see my page https://researchveracity.in... for the reasons which lead me believe that none of these three articles report actual research.

I think that the present article and a recent one An autocrine Vitamin D-driven Th1 shutdown program can be exploited for COVID-19 Reuben McGregor et al. 2020-07-19 https://www.biorxiv.org/con... are important steps in elucidating the role of vitamin D deficiency in COVID-19 severe symptoms. I have cites both articles at my page on vitamin D and COVID-19: http://aminotheory.com/cv19/ .

More research is urgently needed, but since vitamin D is a safe, inexpensive, nutrient which most people are deficient in (by the 40+ ng/ml standards we now know are important for immune system health) robust supplementation programs for all in need (most humans) need not await further research or clinical trials.

On 2020-12-19 17:11:21, user Gary Bayer wrote:

As an actuary whose required training includes construction of mortality tables, life tables and life expectancies, I attempted to verify the results. Unfortunately the details of the methods are too vague to be easily followed, so instead I attempted a standard approach to creating life expectancies. Starting with the 2017 US life tables, I explored modifying the "qx's" (probabilities of death in the next year for an individual aged x) but assuming a one time nature of Covid-19, only the specific current age (and perhaps the following age) should be adjusted for any age cohort. Therefore, for an individual age 10, only the qx for age 10, and perhaps age 11, should be adjusted to reflect the impact of Covid-19 on life expectancies. The age adjustment should be reflective of mortality risk at that age. At this point on time, based on the CDC's reporting of excess mortality, there is no evidence of increased mortality for idividuals under the age of 15. In other words, Covid-19 has not changed this cohort of individuals at all.<br /> The best guess that I can make as to what the authors were trying to express is that Covid-19 has, or is expected to reduce the average age at death this year by a year. I do not know if this is true or not but can see some merit in estimating that result.<br /> One final note, I visit the IHME Covid-19 website almost daily. It is a great tool for seeing the current state of Covid-19 in the United States, and a great tool for policy makers to get insights on what they may need to be planning for in the next couple of weeks. However, a simple look at it's various projections for daily deaths clearly shows the naivety of the estimates of what might happen in the beyond a couple of weeks. An adage that I always rely on as an actuary is the results can only be as good as the assumptions--even if the model being used is good.

On 2020-07-15 13:15:39, user E Y wrote:

Something is wrong here, the IHME projected 2020 total US death is about 250000, that's 0.08% of the US population, how can that cause 1% of reduction of life expectancy of US population?

On 2020-07-25 12:17:53, user John H Abeles wrote:

Hydroxychloroquine ( HCQ ) and Covid19

The negative observational and controlled clinical studies to date refer mainly to using hydroxychloroquine (HCQ) in serious, later stage, hospitalised Covid19 patients

In both the Solidarity/WHO study and the Recovery/UK study extremely high, even massive doses ( up to 6 times that recommended for early CoVid19 patients!) were used for unknown reasons - since the half-life of HCQ is around 21-30 days these daily massive doses could have caused very high blood levels and likely were fatal in some instances - so HCQ group deaths could have been caused by such high dose regimes, so probably skewed the results ..

Also this is likely the wrong group of patients to treat with maximum effect, in the first place — early Covid19 is the best arena for HCQ treatment in combination with zinc and either azithromycin or doxycycline...

It must be stated that no known oral antiviral for outpatients works maximally unless given quite early in disease eg oseltamivir/Tamiflu influenza; valacyclovir/Valtrex in herpes

Even iV remdesivir - a potent SARS-CoV-2 antiviral - didn’t achieve hoped for results in hospitalised patients

Later stage Covid19 patients are mostly suffering from the effects of hyperinflammation ( cytokine storm) and when viral titres are well beyond their peaks. Hyperinflammation can cause myocarditis which can certainly predispose to further cardiac toxicity.

[There are interesting thoughts that the hospitalised patients with cytokine storm / hyperinflammation in reality have a form of ADE ( antibody dependent enhancement of disease ) ie a hyperimmune reaction to a second SARS-CoV-2infection or as a result of a SARS-CoV-2 infection after a previous infection with a closely related virus]

HCQ was also used in the negative studies without added zinc which could be a design for failure, as one of the main, but certainly not only, antiviral actions of HCQ is as a zinc ionophore ie it gets zinc to enter cells much more easily where it can exert its added and established antiviral actions

HCQ is a known antiinflammatory and this action may be of some use in the hyperinflammation stage in hospitalised patients, but other more potent immunosuppressive ( and a few candidates that are nonimmusuppresive immunotherapies) could be more demonstrative in this regard.

Despite this there are some data to suggest benefit of HCQ even in hospitalised patients

For early Covid19 the usually prescribed course is for 5 to 7 days of around 400 mg daily HCQ with 100-200 mg zinc which would not invoke the long term side effects mentioned so often - and very few toxicities are reported even in long term therapy for autoimmune disorders. Any short-term arrhythmia concerns can be allayed by making sure of normal potassium blood levels

In the several thousands of outpatient Covid19 case reports published up to now , when used in early disease, there have been few if any major side effects noted.

(But in later stage, serious hospitalised patients many other drugs are also used, bringing into question the possibility of toxic interactions with HCQ. Also organ damage including myocarditis -heart inflammation-could be a particular predisposing factor in hospitalised patient toxicity predisposition to HCQ )

HCQ is a cheap, easily made generically available drug - and main manufacturers, like Novartis and Teva have donated billions of doses worldwide since the event of Covid19, so shortages, as some fear, for those taking it for malaria ( preventions or treatment) or for autoimmune diseases, like lupus or rheumatoid arthritis etc are highly unlikely

Here below are some pertinent positive references for further reading on the question of HCQ plus zinc plus either doxycycline ( my preferred choice because it isn’t associated with further small cardiac risk) or azithromycin

Note : Most of the successful reports of the use of HCQ plus zinc etc are in early stage, outpatients and not in late stage, hospitalised patients

The first link is a large data base (more than 50 studies ) on HCQ in Covid19 treatment

The second reference is an important review from a Yale University professor ...

The third and fourth are on a recent, large, well conducted observational study from Henry Ford Hospital ...

The fifth is an important outpatient study ...

https://c19study.com/

https://academic.oup.com/aj...

https://www.ijidonline.com/...

https://www.henryford.com/n...

https://www.preprints.org/m...

https://www.ijidonline.com/...

https://www.preprints.org/m...

https://aapsonline.org/hcq-...

https://www.medrxiv.org/con...

https://www.preprints.org/m...

https://www.evms.edu/media/...

https://link.springer.com/a...

https://pjmedia.com/news-an...

https://www.medrxiv.org/con...

https://www.medrxiv.org/con...

https://www.middleeasteye.n...

http://www.ijmr.org.in/prep...

https://aapsonline.org/hydr...<br /> decide/

https://www.indiatoday.in/i...

https://www.medrxiv.org/con...

On 2020-06-25 11:12:33, user Dude Dujmovic wrote:

Faulty study. The BCG cohort is older than non-BCG cohort, likely by 2-3 years on average. That is not a small difference when the samples are so big. Amazing how they did not notice that. There is always a good reason why randomized samples are used. Your samples are biased based on age. You need to have samples with about the same average age and about the same standard deviation. And more.

On 2020-07-25 19:31:23, user ???? ??? wrote:

It reflect the PK/PD pharmacological predication of efficacy <br /> The problem of LPV is complicated PK . Strong protein binding 98 % , extensive metabolism , long list of drug interaction. Therapeutic drug monitoring is mandatory to adjust dose in clinical setting. Moreover extrapolation of in EC50 to the current dose is not prefect. It was suggested to use PBA EC90 . the base line protein binding adjusted 90 % effective concentration. There is a debate about ability of current regimen to achieve Cmax > PBA EC 90 at lung tissues in severe cases

On 2020-07-26 14:06:30, user Gordon Erlebacher wrote:

I started to read the paper, but all the equations are missing. <br /> Here is an additional question. The contact matrix Mij measures to the average number of contacts between one person in group I and all members of group j. But are these different contacts or contacts with repetition? The different possible choices affects the spread of the virus. Any insight is appreciated.

On 2020-07-28 17:02:07, user Liam Golding wrote:

Nice research on an important matter. I really appreciate your work.

It would be nice to know the sample sizes you experimented on to obtain the statistical differences. Can you provide these on request?

Cheers,

Liam G

On 2020-06-30 21:10:32, user Stephen Cherniske wrote:

This is rather paradoxical, in that IL-13 is generally considered to be an anti-inflammatory cytokine, as in IBS. Even more surprising: the observed beneficial effect of DHEA treatment of murine IBS appears to result in part from increased IL-13 expression in colonic epithelial cells. REF: Immunobiology. 2016 Sep;221(9):934-43. doi: 10.1016/j.imbio.2016.05.013. <br /> Dehydroepiandrosterone (DHEA) Restrains Intestinal Inflammation by Rendering Leukocytes Hyporesponsive and Balancing Colitogenic Inflammatory Responses<br /> Vanessa Beatriz Freitas Alves , Paulo José Basso et al.

On 2020-08-03 13:54:21, user Charles R. Twardy wrote:

Forgive me if this is covered in the paper - today I am just skimming abstracts. But another preprint out today shows a mortality risk reduction of 0.7 per 100 kJ/m^2 of ultraviolet (UVA) exposure, in three countries measured at the county level. Is US altitude a proxy for UVA? Vice versa? Could you two combine models to look for residual effects?

On 2020-07-02 15:39:58, user Kamran Kadkhoda wrote:

The reported prevalence is very high suggesting high false positivity rate. The actual sero-prevalence for that county is estimated to be around 6% as of today (if we assume only 20% of cases are tested by for RNA). It would have definitely been much lower back in April. Another reason serology should not be used given it's high rate of false positivity mostly due to common CoVs like OC43 and HKU1.

On 2020-08-04 08:27:16, user OxImmuno Literature Initiative wrote:

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

On 2020-07-02 18:46:51, user Julio C. Spinelli wrote:

Having personally arquitected several clinical trials, the phase II results in young volunteers forces me to provide a word of caution to our collective desire to quickly develop a vaccine for COVID-19. <br /> The frequency and severity of many of the AE's described in this preprint for the young (18-55) and healthy population described here doesn't bode well for the results of a phase III clinical trial. Not until the phase II results of the older cohort are known. Furthermore, extrapolating these data to the Latin and Black populations would be pure hubris on our part. Further phase II data is required before we move into phase III trials<br /> Dr. Julio C. Spinelli

On 2020-07-02 20:46:36, user C'est la même wrote:

The claim of 99.3% specificity seems very high compared to other antibody tests when tested with large population samples.

But that aside, some readers seem to be inappropriately concluding that undersampling in specific regions during that period can be generalised to conclude that the true cumulative incidence is ten times the total number of confirmed cases.

This is unfounded for two reasons. The first is that regions with very high case numbers (Such as NYC) were temporarily overwhelmed in terms of testing capacity and correspondingly very high test-positivity rates. However over time, the testing caught up with demand and with expanded testing, the test-positivity rates dropped by the expected order of magnitude and likely "caught up" for at least some of the participants who were missed.<br /> The second reason is the sample in the study is not a true random population based sample, but a convenience sample which is also biased towards higher test-positivity rates.

Thus while I don't disagree with the conclusion of the authors, I urge strong caution among readers who are tempted to conclude that true case numbers are a magnitude of order higher than officially reported.

On 2020-07-03 20:21:43, user Marm Kilpatrick wrote:

Interesting paper. <br /> Could you clarify if the incidence values in Fig 1,2 and throughout are:<br /> Incidence = Cases in age group X/total population<br /> OR<br /> Incidence = Cases in age group X/population of age group X<br /> Since the age groups represent different fractions of the total populations this would change the intercept of the different incidence values/curves.<br /> Thanks!<br /> marm

On 2020-07-06 18:41:52, user Fatnot wrote:

Unlikely that just zinc supplementation would work,,,a zinc ionophore is also required.. We also have the report from Dr Zelensky, in Rockland County, NY, who treated hundreds with<br /> a combo including zinc and HCQ, resulting that few required hospitalization. The report is anecdotal...but another term for a set of anecdotes is of course DATA And with data and analysis, one can draw conclusions and confidence intervals.

On 2020-08-12 08:06:03, user Marek Kochanczyk wrote:

The source code to analyze data and generate figures as well as updated figures are available at GitHub: https://github.com/kochancz...

On 2020-08-12 12:37:37, user Marc Imbert wrote:

It is worth to not that this study has more cormobity and symptomes for the group treated with HCQ and AZT. All patient not treated has a mild desease while about only 63% in the group treated. Further one should use a healthy scientific scepticims regarding hasting conlusions based on studies at the late stage of the desease. In particular with the description of the evolution of the disease which is now known,

.

On 2020-07-13 11:08:47, user Andrew D'Silva wrote:

In any infection IgM responses converting to IgG responses fall over time and rise when there is a secondary antigen exposure. Why do these findings suggest that there is loss of immunity with declining neutralising antibody levels? Surely the questions are: what happens after secondary antigen exposure? Do the neutralising antibody levels rise again? Do they protect from developing the same clinical disease again? Do they affect severity of disease after second exposure?

On 2020-08-21 19:06:14, user Heather Gray wrote:

I'm a layman but I want to express my appreciation for exploring the issue from this angle

On 2020-08-25 09:01:02, user Tjabbe wrote:

Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.

If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.

On 2020-08-30 11:33:10, user Martijn Weterings wrote:

One problem with those S(E)IR compartmental models is that they always assume/pretend that a virus is spread homogeneously among a well mixed population. According to such models, the chance that someone in a small village in the South infects somebody else, is the same chance for anyone. The same for somebody in the North as somebody in their immediate family or other people in close neighborhood.

Such compartments are obviously not realistic for modeling an entire country. More suitable are networked S(E)IR' or spatial S(E)IR models. In such models, the virus spreads more like an ink blot.

Due to the local saturation, growth rates are already decreasing early on. Models that do not incorporate local saturation will 'compensate' (in order to get the same early deflection) by either reducing R0, or the (effective) population, or the reporting factor (upscaling the number of infected). If you try to fit a simple compartment SIR model to real data, then you will get unrealistic epidemiological parameters.

What they are doing in this article, dividing the population into layers with different rates of infection, is effectively shrinking the population that is 'reached' by the virus.

So this effectively makes the population smaller, but the question is whether it is the right way to shrink the population? Instead of a parameter in a mechanistic model, it might better be regarded as a parameter in an empirical model. It is an extra variable to ensure that the unsuitable simple SEIR model corresponds somewhat better with the measurements.

In reality, there are several effects that cause the observed epidemiological curves to deviate from the simple models (Besides heterogeneity, the use of local distribution in spatial or networked S(E)IR models, instead of global homogeneous compartments, is another important one).

By only including only a single effect in fitting, you get that all other effects are absorbed by that one effect. The result is an unrealistic estimate of the epidemiological parameters, which will not be suitable for extrapolation (for example calculating the 'herd immunity' percentage).

It is to be expected that this model, with only the heterogeneity incorporated, will likely underestimate the percentage to reach herd immunity. This is because it is overestimating the effect to compensate for the lack of other non-incorporated effects (and spatial models will be able to model the same deflection of the curves, but with less reduction of the herd immunity).

The above is a severe systematical problem, which will result in a bias towards smaller herd immunity percentages.

In addition: The fit with the curve is strongly determined by an interaction of the population size and the factor between the reported infections and actual infections (in a simple S(E)IR model, the two have the same effect). Such correlation between the two parameters will cause great inaccuracy.

And these are considerations that do not yet mention the problems with measurements of the epidemiological curve. For instance, the inaccuracies in reporting are not easily solved with a single (constant) reporting fraction. In order to estimate epidemiological parameters we need more direct experimental data (e.g. detailed information about contact tracing). From those we can deduce more directly the variations in infection rates and estimate the potential impact on herd immunity. Just fitting a model to the curve is a bad idea.

On 2020-09-23 07:52:50, user Subhajit Biswas wrote:

Pleased to see other scientists are supporting with further evidences, the trend we had observed and reported as early as April 2020.

Based on non-overlap of dengue and COVID-19 global severity maps and evidences of SARS-CoV-2 serological cross-reactions with dengue, we proposed that immunization of susceptible populations in Europe, North America and Asia (China, Iran) with available live-attenuated dengue vaccines, may cue the anti-viral immune response to thwart COVID-19.

https://www.preprints.org/m...

Our publications in this area to support our proposition:<br /> 1) COVID-19 Virus Infection and Transmission are Observably Less in Highly Dengue-Endemic Countries: Is Pre-Exposure to Dengue Virus Protective Against COVID-19 Severity and Mortality? Will the Reverse Scenario Be True?

Clinical and Experimental Investigations, Volume 1(2): 2-5.<br /> https://www.sciencereposito...

1. Nath, H., Mallick, A., Roy, S., Sukla, S., & Biswas, S. (2020, June 19). Computational modelling predicts that Dengue virus antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?. https://doi.org/10.31219/os...

2. This one in medRxiv!

Now, other scientists are observing the same trend in Brazil! Exciting!

See recent publication below and news coverage

1.https://www.medrxiv.org/content/10....

1. Study suggests dengue may provide some immunity against COVID-19.<br /> https://timesofindia.indiat...

Amazing! Nature has its own ways of controlling parasite aggression! Antigenic correlation between a flavivirus and a coronavirus was unprecedented.

Existing and licensed dengue vaccines could be tested in SARS CoV2 animal models and tried in dengue non-endemic countries.

Use in dengue-endemic countries may be problematic as such vaccination can elicit antibody-dependent enhancement of subsequent dengue infections.

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 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 2020-08-15 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 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 2020-08-24 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.

On 2020-09-10 16:51:51, user Thomas Waterfield wrote:

Thanks Sunil. It was great to chat the other day.

We have produced a protocol that is currently with BMJ Open. The data presented here relates to the first clinic appointments (16th April to 3rd of July) for all participants. The symptom data was reported using RedCap data capture with retrospective reporting of illness episodes prior to the attendance from the beginning of the pandemic in February. In all instances the symptoms were reported without the participant knowing their antibody status. Data were entered by trained members of the research team.

On 2020-09-13 01:19:25, user mzbaz wrote:

There is an unfortunate typo in the horizontal axis unit label of Fig 3b, which should be "minutes" not "hours", consistent with the "15 Min Rule" vertical line, as well as the discussion in the text.

On 2020-09-16 19:31:28, user Ibrahim Hussin wrote:

I believe the precipitation in the weather type of Drizzle has effect on the outbreak

On 2020-09-18 16:27:11, user kdrl nakle wrote:

These types of papers that are masquerading as science are nothing more than speculations. Even IMHE forecasts from this Spring are laughable now. This is in the same venue.

On 2020-09-22 03:41:51, user Direk Limmathurotsakul wrote:

This article has been accepted for publication at the Emerging Infectious Diseases (EID). https://doi.org/10.3201/eid...

On 2020-09-24 10:16:48, user Camila Hobi wrote:

I would like to congratulate the authors for this paper! Wonderful idea! The hypothesis that children can be protective rather than harmful is very plausible! Unfortunately since the beggining of pandemic people are saying the opposite based in misbeliefs and not in science. It’s very important to test this hypothesis in other countries. Reading this paper, I asked myself “why keep schools closed?”

On 2020-09-27 03:43:34, user LB wrote:

It is well known that magnesium absorption is an issue with elevated gastric pH from PPIs. <br /> Please evaluate the possibility that the individuals who had a history of taking PPIs might have had magnesium deficiency, which altered their immune response to SARS-CoV-2.<br /> - Linda Benskin, PhD, RN

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

It would be be helpful to know the dosage.

On 2020-10-04 20:57:37, user William James wrote:

Do you think you should refer to the paper by Kojaku at al [Submitted on 5 May 2020 (v1), last revised 14 Sep 2020 (v3)] at https://arxiv.org/abs/2005...., which largely represented this analysis formally some months earlier? [I have no conflict of interest.]

On 2020-10-15 22:40:24, user Marm Kilpatrick wrote:

Dear Dr. van Beek and co-authors,<br /> Thank for your this important work!<br /> In your Table 1 you appear to be grouping results for multiple assays together:<br /> Panbio™ COVID-19 Ag rapid test (Abbott), and Standard Q COVID-19 Ag (SD Biosensor);<br /> and COVID-19 Ag Respi-Strip (Coris BioConcept), and GenBody COVID-19 Ag (GenBody Inc)<br /> I *think* you did this because they had similar LODs but it'd be more informative if you could show results for each assay independently. <br /> It would also help to know the sample sizes for each of the assays in each group of patients.<br /> Finally, specificity is a potential issue with these rapid antigen assays. Did you test samples that were negative by PCR to determine this (acknowledging that PCR could miss viral RNA, especially if not done at the same time)?<br /> thank you,<br /> marm

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 2022-10-24 17:42:56, user CDSL JHSPH wrote:

Dear Mekkes et. al.,

Thank you for sharing your work with us! Creating models to predict neuropathological diagnosis based on temporal signs and symptoms is very significant research, and I’m looking forward to seeing where this heads in the future! I enjoyed reading this paper, especially since it introduced me to techniques and concepts I was previously unfamiliar with. That being said, while reading I did notice some parts that I think could be given further clarity in order to make this paper more accessible to those not within the immediate field. There are a lot of abbreviated disorders mentioned, and I noticed that some were explained in the introduction however I could not find the proper matching terms for disorders abbreviated in later sections. I think it would be a great benefit if there were a word key with the disorders and their corresponding abbreviations. Especially since different disorders may be represented by the same acronyms, so googling it may not provide the reader with the correct one. Also, I was wondering if you plan to do another study focusing on optimizing these models to diagnose mental illnesses and psychiatric conditions in a separate paper? I understand the main focus on brain disorders and neurodegenerative diseases since those can be linked to prominent neuropathological changes, but when reading the abstract I was given the impression that mental illnesses would be focused on to a larger degree than I noticed in the paper. I would love to see any future steps you take with this, especially since the alterations in cognition and behavior associated with mental illnesses can be observed from live patients, and don't necessarily have to be inspected retroactively like from brain donors.

On 2022-10-24 18:20:28, user Jordan Ross wrote:

This study was aimed to understand the complexity of diagnosing brain disorders in society today. The author utilizes data in statistical analyses to identify this crossover and give rise to further research in the field. This journal article emphasizes the use of a computational pipeline to establish a series of clinical synopses regarding a series of brain disorders from donors that were previously diagnosed. Donors from the NBB underwent a series of cross-disorder research to identify signs and symptoms associated with psychiatric diagnoses. Overall the article had great analyses and synthesis of ideas pertaining to the mechanisms of symptomatology and how it differentiates across brain disorder profiles. One thing to note is organization in this article. Maybe incorporate these main figures within the results section with titles and keys for each to limit confusion to the reader. Additionally, in the methods section (2.7.3 Observational profiles of the signs and symptoms), the author referred to a figure but did not implicitly state which one. It is rather stated as: (Figure number?). This should be better examined. Lastly, the addition of implicitly stating the need for further research is a great way to highlight the need for experimental study amongst the reader. With this, it would be helpful to highlight your targeted audience within this article (i.e. professional, student, etc.). If you have a targeted audience with a scientific background you will not need to go into further depth to define terminology and specific neurological pathways in this study. Overall, job well done and very interesting study!

On 2022-11-08 15:26:46, user W_R_1 wrote:

Hi,

Thanks for the very interesting and useful paper!

I wondered if the stated z-value calculation (p26, next-to-last line) has a typo of a "-" sign rather than a "+" in the denominator?

I was also curious as to the properties of this test statistic in general, given that the two beta estimates aren't necessarily independent (i.e. when calculating the variance of the difference between the two beta estimates, their covariance can't necessarily be assumed to be 0?)?

On 2022-11-26 06:41:30, user Soichiro Obara wrote:

Dear Prof. Schindler,

First I (Soichiro Obara, the PI of this project) apologize for being replying to your great comments.

Second, our project has modelled the European APRICOT as you commented. We would sincerely appreciate your suggestions from the viewpoint of the author of the APRICOT.

On this matter, before launching this project, we got in contact with the principal investigator of the APRICOT, Prof. Walid Habre, who has been giving many suggestions and comments to us.

As you might have concerns regarding data acquisition, he also gave comments as to how tough the data acquisition and cleaning in the APRICOT.<br /> Hence, this time, we assume that we may need to change the case report form (actually modelling the CRF of the APRICOT) after a pilot study which hopefully will be conducted February or March in 2023.<br /> (I have heard from Prof. Habre that a pilot study was conducted in three centers to examine the feasibility of the protocol and the CRF in the APRICOT.)

If possible, I would appreciate your "specific and direct" suggestions as to which data items in the APRICOT might be difficult or be unnecessary to collect.

Again we would sincerely appreciate your great suggestions and comments, if you kindly gave, in the future again.

I am very pleased if you kindly get touch with me on behalf of the Asian Society of Paediatric Anaesthesiologist research committee.

Soichiro Obara (Japan)<br /> e-mail address: soichoba1975@gmail.com

On 2022-12-09 13:17:21, user Maja wrote:

I applaud the authors for their meaningful work on reporting COVID-19 trials. The results are fascinating. Only 19% of COVID-19 clinical trials were published within three months of completion, which shows how much work has to be done regarding research transparency, publishing trial results, and responsibility while conducting clinical trials. Timely publication of results and avoiding research waste in clinical trials should be a priority, especially during global public health emergencies, such as the COVID-19 pandemic.

On 2022-12-27 15:27:14, user nobiggie wrote:

The most interesting piece of information here seems to be that the more shots you get the more likely you are to get sick but somehow it goes unmentioned

On 2023-01-06 12:54:34, user Mike Verosole wrote:

A more recent, peer reviewed publication involving a much larger population set showes that indeed vaccinated people were more likely to be infected vs previously infected individuals, however, the vaccinated group was about 25-35% less likely to be hospitalized or experience death. Maybe this paper will be reviewed in light of this new publication

On 2023-02-19 17:11:56, user citrate reiterator wrote:

The simplest explanation for the vaccine dose trend is that it’s not broken out by previous infection status and date. Other data in the paper confirms that recent past infection is very protective against the omicron subvariants. The more shots you have, the more likely you probably were to be omicron-naive at the start of the study period. As Luis Cruz points out below, even past research from this same group has not previously found a dose-response relationship of this type. Also, when they actually fit a model that takes these confounders into account (the proportional hazards model), they find that there is a modest preventative effect of the bivalent booster — which is not consistent with a dose-dependent increase of risk following vaccination.

On 2023-01-16 01:47:52, user Brian Piper wrote:

This is a timely review on an important topic!

In evaluating the safety of the relaxation of take-homes, it might be helpful to consider that “The number of clients receiving methadone increased from 306,440 in 2011 to 408,550 in 2019 and then decreased to 311,531 in 2020” [1]. This reduction from 2019 to 2020 of 97,019 patients is a 23.7% decline!<br /> The increase in the number of overdoses involving methadone may only appear equivalent to the increase in all opioid overdoses when one does not take this factor (i.e. using the population size and not the number of methadone patients as the more appropriate denominator) into account.

The authors also are encouraged revisit the important policy research in England and Scotland which identified sizable declines in methadone overdoses following implementation of supervised administration [2].

Citations

1. National Survey of Substance Abuse Treatment Services (N-SSATS): 2020 Data on Substance Abuse Treatment Facilities. Table 3.2.

2. Strang et al. Impact of supervision of methadone consumption on deaths related to methadone overdose (1993-2008): analyses using OD4 index in England and Scotland. BMJ 2010;341:c4851.

On 2023-02-09 23:16:58, user Constantinos C wrote:

The published version can be accessed via the following link: https://www.nature.com/arti...

On 2023-02-20 07:41:20, user Martin Schecklmann wrote:

I miss in the discussion the meaning of relevant dose of TMS with respect to e-field. We could demonstrate that F3-based method is similar to neuronavigation-based method with respect to clinical efficacy even if induced e-field was lower for F3-method (doi: 10.1016/j.brs.2021.01.013). In addition your sample size is very low and correlation between clinical efficacy and e-field may be biased by outliers (figure 3).

On 2023-02-22 16:16:10, user Robert Clark wrote:

Major flaw in your analysis. <br /> You noted the report by Peterson et al included cardiac arrest cases where they survived:<br /> “This study identified 173 confirmed SCD cases (and 158 SCA cases with survival), so on average 43 SCDs per year.”

Since there has been a great increase in awareness of cardiac arrests and arrhythmia in athletes there have been a great increase in AED’s (automatic defibrillators) available and those trained in CPR.

Then many of those cases even in just the last couple of years survived who would have died previously. So you should have also counted the number of cases who had cardiac issues who also survived in the current, pandemic era.

Additionally Maron et al described other causes other than just cardiac arrest as the cause of the sudden deaths. Since the Goodsciencing report considers several kinds of serious life threatening illnesses arising in athletes you should also have done a separate count of those cases in the Goodsciencing report, again both of those who died and those who survived.

Robert Clark

On 2023-03-02 18:33:37, user Daniel Park wrote:

Fascinating. Lines up well with other evidence including the Lewnard et al. study showing interactions b/w pneumococcal carriage and SARS-CoV-2. We also saw similar patterns with viral load (+ severity) and pneumococcal carriage with human endemic CoVs: https://journals.lww.com/pi...

On 2023-04-04 06:16:50, user Kevin J. Black wrote:

I think you'll want to examine the spatial delayed response task, which Tamara Hershey and other have studied in Parkinson disease, e.g. https://pubmed.ncbi.nlm.nih...

On 2023-05-16 21:45:14, user Maria Log wrote:

This is really very strange and disappointing that you consider the use of private e-mail as an indicator of fake articles. Since my years as a PhD student I always use, and I use now and will use my personal email in all my articles. There are many reasons for that, but the most crucial one is that I (as well as any other researcher)can change my affiliation and then my institutional e-mail will be inactivated and ones who are interested in my publication will not be able to get an answer to their questions. So the use of an email linked to the organization is unfair and irresponsible. Here is a link to my google scholar profile: https://scholar.google.com/... - good luck with finding any fake articles!

On 2023-05-24 14:24:40, user Shaffi Fazaludeen Koya wrote:

The article is now published in Lancet SEA journal,<br /> https://www.thelancet.com/j...%20in%202016%E2%80%932020 "https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00130-5/fulltext#:~:text=The%20pooled%20hypertension%20control%20rate,28.0%25)%20in%202016%E2%80%932020").

On 2023-06-07 16:45:42, user Nathan Pearson wrote:

In this study (of American patients to whom bivalents were available only as booster), all bivalent recipients by definition got 3+ total mRNA vaccine doses, while the current preprint text's control group got 2+.

As such, to compare equal dose counts (if not timing relative to prior waves etc.), can the authors please add a sub-analysis of peer bivalent (original or BA.4/5) vs. 3+ dose (not 2+) monovalent recipients?

Not doing so inherently confounds any additional benefit of bivalent vs. monovalent formulation with a group difference in total doses per participant.

Thanks

On 2023-06-07 17:10:10, user Nathan Pearson wrote:

Also: in adding such needed apples-to-apples comparison of bivalent vs. monovalent peer boostees (i.e., with 3+ doses in each otherwise background-similar group), can the authors take care to control for time since last dose?

I.e., if bivalent boostees got boosted at most k months before data freeze (so were tracked for severe COVID for <=k months after last dose), likewise tally cases of severe COVID in monovalent boostees <=k months (but not longer) after their last dose.

Thanks.

On 2023-06-22 16:56:53, user Benjamin Shuman wrote:

This is interesting work. The descriptions of methodological variation (task, muscles measured, amplitude normalization method) certainly impact how the studies compare to one another. However, there is no discussion of filter parameters and its impact on the interpretation of synergy metrics. With more aggressive filtering the resultant processed EMG signal is less variable which also has a direct impact on the number of synergies extracted or tVAF1/DMC. The Collimore article is noted for having the least number of synergies identified but also has the most aggressive LP filtering (4hz). There may be additional trends in the article findings when looked at through a filtering lens and I would encourage the authors to consider this. Finally, please note that DMC is a linear transformation of tVAF1 (Steele 2015). As such the trends in DMC are directly comparable to tVAF1.<br /> Thanks,

On 2023-07-10 13:53:43, user Carlos Menck wrote:

This work was published in Carcinogenesis and the correct citation is: <br /> Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, SarasinA, Menck CFM. Mutational signatures and increased retrotransposon <br /> insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print. <br /> PMID: 37195263.

Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, Sarasin A, Menck CFM. Mutational signatures and increased retrotransposon

insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print.

PMID: 37195263.

On 2021-12-21 15:35:35, user Greg Mock wrote:

Has any association of PANS and the NOD2 gene been studied?

On 2023-10-24 02:18:23, user CDSL JHSPH wrote:

Dear Dr. Bi et al,

Thanks to your work on influenza, which has provided a new proof that the residual repeat vaccination effect might be explained by different rates of subclinical infection between repeat and non-repeat vaccinees via two proposed mechanisms, the infection block hypothesis and enhanced vaccine immunogenicity and protection post-infection.

As a reader who doesn't know much about the field ,I can give you some reading feedback for your reference.<br /> First I think your article provides three important pieces of evidence. <br /> 1,Repeat vaccinees were vaccinated earlier in a season by one week.<br /> 2,Clinical infection influences individuals’ decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type.<br /> 3,Adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination.<br /> 4,Adjusting for subclinical infection could theoretically attenuate this effect.

On the basis of your good work, I would like to offer a bit of advice for readers who are not experts in this area. First is the article structure section. I hope this provides some perspective to help you publish. First of all the name of the title may be too long for non-specialized readers. It may lose some of the attention. Furthermore the explanations of within-season waning, recent clinical infection, and subclinical infection could have come in the INTRODUCTION instead of the METHOD before being mentioned. Another thing is that I think you could put in the conclusion that has some summarizing words underneath the fig so that readers might find them easier. Also the part you put in the appendix about theoretical modeling some of it is necessary for understanding the model, if you could summarize it necessary and put it in the body methodology would help understanding.

The next aspect is about research, first of all your work is very relevant and on this basis you are perfectly placed to capitalize on this aspect. First of all you can try to find some way (like sampling) to compare your theoretical model with the results of the data response you got, so that you can speculate about the effect of the vaccine in reality and the possible number of Subclinical infections.

After that comparing your model with real data would be an interesting aspect. And this can emphasize the correctness of your model and increase the credibility of your article. Although overall this article has been highly relevant with enough realistic data.

However, on this basis, one can consider whether the data from different regions (the five regions sampled) are very different? For example, are the probabilities of tuning into a vaccination strategy similar in different regions, and do repeat vaccinees in each region tend to get vaccinated earlier in the flu season than non-repeat vaccinees? These comparisons of data from different regions can be added to the article as they relate to the reproducibility and generalizability of your model's and conclusions.

Finally, thank you for this article, which provides very good evidence for the causes of Reduced effectiveness of repeat influenza vaccination, and this article has the advantage of incorporating a lot of details that were not considered in previous studies and provides a good interpretation of the errors, providing new ideas and theoretical models for the field. And I personally learned a lot of research ideas from you through this article, thank you for your work!

On 2023-11-03 16:41:46, user Dr. Hans-Joachim Kremer wrote:

It is not well described, what exatly is control. The estimates in Figure 1 are not comprehensible given the few details provided. <br /> Fig 1: It is written that Pfizer bivalent vs. Moderna bivalent is shown, however, the text suggests that Pfizer bivalent was compared with its own contol (before vaccination). What is true? If in fact you compared them to their own control: Where are respective details?<br /> It appears that in that Figure the "all" in the first row has a different meaning from the "all" in the third row. Note that the "16" of the first row, denoted as "all", is repeated in the fifth row, but denoted as "new".<br /> Anyhow, the "new" is not explained in the text and the "all" might have a meaning different from common understanding, as it appears that it stands for "after matching", so not really "all".

On 2023-12-07 15:20:52, user Alex Liber wrote:

This article has now been published: Liber AC, Faraji M, Ranganathan R, Friedman AS. How Complete Are Tobacco Sales Data? Assessing The Comprehensiveness Of US Tobacco Product Retail Sales Data Through Comparisons To Excise Tax Collections. Nicotine Tob Res. 2023 Nov 2:ntad214. doi: 10.1093/ntr/ntad214. Epub ahead of print. PMID: 37933997.

On 2023-12-07 19:39:10, user Chihua Li wrote:

The study has been published: https://bmcmedicine.biomedc...

On 2023-12-14 17:58:32, user Michelle Carras wrote:

Very curious about this. It's worth considering, but the authors had some big challenges with their sample, which is a relatively well-off, very White population, a bit older than the US average. They have a very high rate of premorbid asthma (about 2-4x the usual), but the thing I find most curious is the use of naltrexone (20% of the sample) and ivermectin (18% of the sample) to treat these new symptoms. Since these uses are very off-label, it makes me wonder about the types of patients who get them. Same with intermittent fasting as a non-pharmacological treatment. I'd be interested in seeing other responses to this and whether it ultimately gets published.

On 2024-01-04 18:43:48, user John Beach wrote:

As a non-doctor with no opinion on ivermectin, I read<br /> "Effect of Ivermectin 600 ug/kg for 6 days vs Placebo"<br /> to learn.

My questions:

1.a.) What is being compared to what?<br /> Placebo isn't identified in the paper or the Supplemental material. Where can I look up the substance used as placebo?

1.b.) Does the placebo contain folic acid or folate? Or a placebo containing Magnesium, Sodium, or Potassium in some form?

Reason for question 1: is it possible the study assumed the placebo was inert, but fortuitously discovered instead another treatment that works against COVID with efficacy equal to ivermectin?

2.a.) Is there a supplement-to-the-study that shows the outcomes sorted by Cytochrome polymorphism?

Reason for question 2:<br /> CYP polymorphisms can affect how our bodies respond to virus.<br /> Some CYP polymorphisms can prevent a patient from even metabolizing the treatment.<br /> Furthermore, CYP3A4 mediates metabolism of ivermectin, but COVID-19 reportedly decreases metabolism of drugs by CYP3A4

e.g. via GoogleScholar, I found<br /> https://tandfonline.com/doi... <br /> "The impact of COVID-19 infection on cytochrome P450 3A4-mediated drug metabolism and drug interactions"

Back to my questions...

2.b.) Is there a way to determine if ivermectin was metabolized by a study participant, and if the molecule was circulating in their system? If so, was there any variability in study outcomes when those outcomes were sorted by Cytochrome polymorphism?

3.a.) Has any past research shown that the placebo ingredients interfere with any of the following?

• Phospholipase PLA2
• Lipid pathways
• Glycolysis, e.g. <br /> PKM2 (pyruvate kinase muscle isoform 2)
• lactate dehydrogenase
• pyruvate kinase
• glyceraldehyde phosphate dehydrogenase

If so, evidence suggests that these, and therefore the intended placebo that affects these, change human biology relevant to the COVID disease process.

It looks like a lot of good work was put into the Duke study, and so I am trying to learn from it. Any answers you can provide are much appreciated.

On 2024-01-16 18:40:48, user Weina Jin wrote:

This is the initial paper preprint. The published paper is at: http://dx.doi.org/10.1016/j.artmed.2023.102751

The updated preprint and supplementary materials S1-S3 can be found at:<br /> https://www2.cs.sfu.ca/~hamarneh/ecopy/aiim2023.pdf<br /> or <br /> https://github.com/weinajin/multimodal_explanation/tree/main/neurosurgeon_clinical_study_data_analysis/paper_preprint

Video paper presentation:<br /> https://www.youtube.com/watch?v=rhLQQsc8Z8Y&t=29s

On 2024-02-03 10:02:01, user Ryan wrote:

published https://rdcu.be/dxHNq.

Benjamin, R. Reproduction number projection for the COVID-19 pandemic. Adv Cont Discr Mod 2023, 46 (2023). https://doi.org/10.1186/s13...

On 2024-03-18 13:01:53, user Varsha Gouthamchand wrote:

Follow the link https://doi.org/10.1093/bjr... for an extended and published version of this article.

On 2024-04-27 22:04:26, user Linette Roungchun wrote:

I am a TSW sufferer and this work is important. I have suffered with TSW on and off since the young age of 3, so to see the science backing up the fact that TSW is in fact, a separate entity from Eczema is so validating and healing. Huge thank you to Kelly, Kathy, and Dr. Ian Myles!

On 2024-04-28 00:05:10, user pfwindsor wrote:

Thank you for the opportunity to comment on this excellent TSW study. I am not a scientist, but have more than 10 years of personal experience with TSW and have studied and published material through the American Academy of Dermatology. I also moderated ITSAN’s online forum for several years where I reviewed thousands of posts and responded to thousands of questions about TSW. I am currently involved with ITSAN research advocacy and would be happy to offer additional input.

Line 33 and Line 66 refer to “prolonged exposure” and “longterm use” of TCS. While my own experience with TSW involves longterm use of TCS, the anecdotal data suggests that some TSW cases follow a short term course of TCS treatment, such as for poison oak. Others report TSW symptoms emerging after decades of TCS use. Our theories are centered around multiple variables for susceptibility to TSW including, but not limited to length of use. We also considered potency, quantity and length of exposure and areas of application. Your study doesn’t seem to address these other potentially significant variables.

Line 67 further states that TSW is related to “abrupt cessation” of TCS use. This may be the case for your small study cohort, but it has not been studied or established within the TSW community.

Line 73 -74 states that “improvement is seen through avoidance of TCS therapy”. This is misleading. Overwhelming anecdotal evidence suggests that TSW symptoms that emerge after cessation of TCS become more acute before they resolve.

Line 306-307 suggests a threshold of 4 months of TCS exposure. Is this an arbitrary threshold identified for purposes of your study or is there a source that proposes or establishes this threshold? I am not familiar with any particular exposure timeframe associated with a risk of developing TSW. In fact, given the anecdotal evidence of TSW with short term exposure, this may not be a valid metric.

On 2024-04-28 21:04:46, user Marly Peel wrote:

PLEASE! PLEASE! PLEASE do more research & acknowledgement of this TERRIBLE problem! I just recently discovered info on this & it all matches my symptoms exactly. Imagine a horrific itch that you cannot scratch enough to make it STOP! I don't think I've ever been this close to panic as I have during those times. There's no way to sleep or rest AT ALL when this is happening. That doesn't even include the flaking, shedding, red & raw areas, irritated & antsy feelings one can have. WE NEED HELP! Plus, there needs to be a HUGE WARNING included with every top.steroid...that doctors know about & take to heart. Top.steroids might be a quick "fix"...but they DO NOTHING to address the underlying reason for the problem in the first place. It's time for common sense to COME HOME!

On 2024-04-27 18:39:09, user Haley DelPlato wrote:

As a young adult whose life has been put on hold for the past 3 years due to Topical Steroid Withdrawal, I can't thank you enough for this work!

Seeing studies about TSW not only helps validate my pain that so many medical professionals have dismissed, but also contributes greatly to advancements in dermatopathology looking forward. The current stigma that makes TSW such a controversial concept NEEDS to be eradiated, a complex task that ultimately relies on substantiated clinical proof to combat misinformation. Unfortunately, the current scope of dermatopathology has kept so many folks unaware of TSW and trapped in harmful cycles of topical corticosteroid addiction. I hope this will be the first of many legitimate works seeking to uncover the truths about this tragic condition so future generations will be believed, treated, and cared for with dignity, in ways the current dermatological standards simply haven't allowed for.

Appreciate the strides this study has taken toward a more compassionate reality for TSW sufferers!

On 2024-04-28 12:21:49, user Henry wrote:

How much Berberine is required for the improvements to begin? Really interested in this study, thank you!

On 2024-04-28 19:43:09, user Laura Mihalidesz wrote:

I am a TSW sufferer and made a signifacant change to the worse in my condition when TS were introduced to my life 30 years ago, stopping the usage a age 26 and going into TSW the symptoms are uncoparable to eczema. This study is important to understand what harm can TS cause in the long run and after 9 years in TSW I still suffer from symptoms. The importnce of this research is not a question but answer to many patient's questions and finding and developing treatment methods.

On 2024-05-07 17:04:04, user Katy Ross wrote:

There is a current flawed premise that ‘adherence’ to steroid treatment is a good idea and anything querying whether that’s a good idea is deemed to be a phobia, as opposed to a clear, rational and well reasoned concern.

I am out the other side of topical steroid withdrawal syndrome, and have lost a lot to the trauma it brings (in all matter of ways). Sufferers like me are desperate for science-backed research, and it’s great to see progress here. It will be fantastic when a diagnostic code has been approved and further research is available to differentiate TSW from other skin conditions so that patients can be acknowledged and treated appropriately.

The former Chair of the British Dermatology Council said that as many as 1 in 10 patients may have TSW and it may be seriously under-diagnosed, and the hashtag #TSW has racked up more than one billion views on TikTok - I applaud anyone who’s working towards ending this huge amount of unnecessary suffering.

On 2024-05-10 04:50:19, user Kaitlin Lee wrote:

Thank you for this long overdue research. It gives me hope that in the near future TSW will be universally diagnosable, treatable, and most importantly, prevented. I have had eczema for ten years, just a few patches here and there, which never interfered with my livelihood. Now 5 months into TSW, so many things have been put on hold for me. People suffering from TSW need treatment and support and medical professionals need to start acknowledging this condition.