Remdesivir for the Treatment of COVID-19: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background The benefits of remdesivir in the treatment of hospitalized patients with Covid-19 remain debated with the National Institutes of Health and the World Health Organization providing contradictory recommendations for and against use. Methods We performed a systematic review of randomized controlled trials (RCTs) of remdesivir for the treatment of hospitalized patients with COVID-19. The primary outcome was mortality, stratified by oxygen use (none, supplemental oxygen without mechanical ventilation, and mechanical ventilation). We conducted a frequentist random effects meta-analysis on the risk ratio (RR) scale and, to better contextualize the probabilistic benefits, we also performed a bayesian random effects meta-analysis on the risk difference scale. Results We identified 8 randomized trials, totaling 9157 participants. The RR for mortality comparing remdesivir versus control was 0.71 (95% confidence interval [CI] 0.42-1.22; I 2 =0.0%) in the patients who did not require supplemental oxygen; 0.83 (95%CI 0.73-0.95; I 2 =0.0%) for nonventilated patients requiring oxygen; and 1.19 (95%CI 0.98-1.44 I 2 =0.0%) in the setting of mechanical ventilation. Using neutral priors, the probabilities that remdesivir reduces mortality were 74.7%, 96.9% and 8.9%, respectively. The probability that remdesivir reduced mortality by more than 1% was 88.1% for nonventilated patients requiring oxygen. Conclusion Based on this meta-analysis, there is a high probability that remdesivir reduces mortality for nonventilated patients with COVID-19 requiring supplemental oxygen therapy.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".