Absolute Risk Reductions in COVID-19 Antiviral Medication Clinical Trials
Bibliographic record
Abstract
COVID-19 antiviral medications approved or authorized for emergency use by the U.S. Food and Drug Administration are reported to have high efficacy in preventing severe illness, hospitalizations, and deaths. However, reports for some of these antivirals use relative risk reductions from clinical trials without absolute risk reductions. The present paper reappraises recently published clinical trial data for the COVID-19 antivirals paxlovid, remdesivir, and molnupiravir, and reports absolute risk reductions, relative risk reductions, as well as number needed to treat to reduce severe illness, hospitalizations, and deaths. Relative risk reductions are 88.88% for paxlovid (95% CI: 72.13–95.56%), 86.48% for remdesivir (95% CI: 41.41–96.88%), and 30.41% for molnupiravir (95% CI: 0.81–51.18%), while absolute risk reductions are much lower at 5.73% for paxlovid (95% CI: 3.79–7.68%), 4.58% for remdesivir (95% CI: 1.79–7.38%), and 2.96% for molnupiravir (95% CI: 0.09–5.83%). Low absolute risk reductions and the high number of patients needed to treat to reduce severe COVID-19 infections, hospitalizations, and deaths challenge the clinical efficacy of antivirals approved or authorized by the U.S Food and Drug Administration. These findings apply to other populations with similar control event rates. Accurate information should be disseminated to the public when selecting treatments for COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".