Absolute Risk Reductions in COVID-19 Antiviral Medication Clinical Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it