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Noticed some inconsistencies for this paper compared to the trial registration: Study Details | Effectiveness of VIRTUAL SFCAMINA STUDY | ClinicalTrials.gov The registration said that they would use "the Fukuda and Holmes classification criteria for Fatigue Syndrome Chronicle" as inclusion...

Something different: On the Retraction Watch article on the Cochrane review, I noticed this comment: I wonder if this is Meredith Warshaw Center for Biostatistics in AIDS Research, Harvard School of Public Health ‪Meredith Warshaw‬ - ‪Google Scholar‬

If I recall correctly the planned update did not require a note with a new DOI. So it's rather strange to argue that they wanted a new citation to highlight the cancellation of the update.

The paper says they added multiple factors in the statistical model which weren't mentioned in the protocol: The protocol says they would correct for the false discovery rate (FDR): But I don't see this in the paper.

Had a look at the protocol which said: So 50m was originally viewed as the minimum important difference? Reference 14 is: Minimum clinically important improvement for the incremental shuttle walking test | Thorax Which says:

Indeed, the data and figures look the same. Strange situation. Normally journals forbid submitting the same manuscript elsewhere.

Thanks for highlighting (the flaws with) this study David. Minor issue: I got lower drop-out rates: 11/56 (20%) in the face-to face group and 17/63 (27%) in the remote group. Strange that they don't report the data for the primary outcome of the control group. I suspect that the control...

Thanks, got the same, only one that met the 5*10^-8 threshold. Have replied this to the researchers. Curious what the explanation will be.

Yes but not sure if I can copy their reply so I'll try to summarise. They basically said that they did not use the 102 significant SNPs highlighted in the publication by Howard et al. (2019). Instead they screen the full dataset where summary data for all tested SNPs is listed. They don't say...

Strange that they don't describe the selection method a bit more: were these 40 patients selected because they had the most severe or most notable cognitive symptoms? So they likely were not representative of ME/CFS patients as a whole?

These seem like the main results: The frequency of their signatures was not increased in the AoU Long Covid cohort if they included signatures with low frequency, instead of those that appear in at least 4-5% of cases.

The authors also refer to a fibromyalgia study where 75% of self-reported cases did not meet diagnostic criteria: Reference 14 seems to be a study by Brain Wallit and colleagues: Three-Quarters of Persons in the US Population Reporting a Clinical Diagnosis of Fibromyalgia Do Not Satisfy...

This suggests the agreement between self-reported ME/CFS and ME/CFS as assessed by surveys such as de DePaul Symptom Questionnaires was not very high. The authors call it 'fair' but I don't think a cohen's kappa (which ranges from -1 to +1) of 0.29 signals adequate reliability. Almost half...

The normal threshold for statistical significant in GWAS is 5 x 10^-8. Because the sample size here was very low (only 695 PTLDS cases), the authors only highlight SNPs that had a p-value below 5 x 10^7

This is where the data came from: But for ME/CFS it seems that they used questionable ICD codes: ICD-10-CM R53.82 refers to 'Chronic fatigue, unspecified'. ME/CFS is listed at G93.3.

Paper from 2016 by the Hanson group. I wanted to post it here because it had a large sample size (large for ME/CFS research, probably small for genetics research). Abstract Background: Mitochondrial dysfunction has been hypothesized to occur in Myalgic Encephalomyelitis/Chronic Fatigue...

From what I remember it didn't not have very problematic statements regarding GET/CBT or psychosomatic theories. But it was also not very precise on biomedical research findings (frequent overstatements).

Would be interesting to see the results of the ME/CFS groups combined (mild-moderate and severe) versus controls. I couldn't easily find that in the paper or supplementary material. I couldn't find the dataset there, anyone been able to find it?

These arguments from the response by Joanna Moncrieff also make sense: Challenging the new hype about antidepressants | Joanna Moncrieff

Happen to be reading this older 2018 study, great to see that there's already a S4ME thread for it. Although this meta-analysis found an effect, for most antidepressants it was notably small. The paper states: This is an overview from the supplementary material (page 150). For Prozac...