I took the liberty of translating @ProfDrMarcelo Santos’ devastating demolition of the latest IVM study by Flavio Cadegiani et. al.
“Follow this thread for critical scientific points to show how bad this study is (and possibly yet another hoax) cureus.com/articles/82162…
Review began 01/04/2022
Review ended 13/01/2022
Published 15/01/2022
9 days to review and respond to reviewers + 2 days to be published. This is impossible in a peer review. At a minimum, this would take 14-21 days (standard time given to reviewer accepting the job)
The title cites 223,128 participants, when in fact, they included 159,561 (63,567 less than what they say in the title). The confusing flowchart is another bad point of the study.
Table 1 further reinforces how misleading the title is! After adjusting for propensity score matching (PSM), that giant N drops to 6,068 (3,034 per group). There goes another 217,060 individuals!
They cite CONEP registration (4,821,082), CAAE: 47124221.2.0000.5485. The study was registered on 05/25/2021 and the data collected between July 7th to December 2nd 2020. So this suggests that they had access to the data even before they registered the request for use.
In the paper they state: "Human subjects: Consent was obtained or waived by all participants in this study". How many of the 223,128 signed a consent form? Either the CEP/CONEP requires a signed term or they approve without a term. This seems very strange to me!
One of the most confusing parts (I've never seen it in science!): "The present retrospective analysis of the prospectively collected data...". Is the study prospective or retrospective? So the title is misleading once again by suggesting that the part. were followed prospectively
More considerations: the study has no a priori record, which may facilitate protocol deviation; It has no defined primary outcome; It does not cite calcul. and sampling power; It does not cite eligibility criteria; It does not mention follow-up time; It does not cite adverse effs
Two statisticians to analyze the data and a 3rd to assess discrepancy. Well-collected and properly analyzed data do not lie and do not generate discrepancies; but if you torture the data they can confess whatever you want!
Finally, outcome analyzes are suspect to say the least. 67% (RR, 0.33; 95% CI, 023-0.66; p<0.0001) and 70% (RR, 0.30; 95% CI, 0.19-0.46; p<0.0001) reduction in hospitalization and mortality, respectively. "Too good to be true" for this effect size!

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