Antiviral drug treatment for nonsevere COVID-19: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Randomized trial evidence suggests that some antiviral drugs are effective in patients with COVID-19. However, the comparative effectiveness of antiviral drugs in nonsevere COVID-19 is unclear. METHODS: We searched the Epistemonikos COVID-19 L·OVE (Living Overview of Evidence) database for randomized trials comparing antiviral treatments, standard care or placebo in adult patients with nonsevere COVID-19 up to Apr. 25, 2022. Reviewers extracted data and assessed risk of bias. We performed a frequentist network meta-analysis and assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. RESULTS: We identified 41 trials, which included 18 568 patients. Compared with standard care or placebo, molnupiravir and nirmatrelvir-ritonavir each reduced risk of death with moderate certainty (10.9 fewer deaths per 1000, 95% confidence interval [CI] 12.6 to 4.5 fewer for molnupiravir; 11.7 fewer deaths per 1000, 95% CI 13.1 fewer to 2.6 more). Compared with molnupiravir, nirmatrelvir-ritonavir probably reduced risk of hospital admission (27.8 fewer admissions per 1000, 95% CI 32.8 to 18.3 fewer; moderate certainty). Remdesivir probably has no effect on risk of death, but may reduce hospital admissions (39.1 fewer admissions per 1000, 95% CI 48.7 to 13.7 fewer; low certainty). INTERPRETATION: Molnupiravir and nirmatrelvir-ritonavir probably reduce risk of hospital admissions and death among patients with nonsevere COVID-19. Nirmatrelvir-ritonavir is probably more effective than molnupiravir for reducing risk of hospital admissions. Most trials were conducted with unvaccinated patients, before the emergence of the Omicron variant; the effectiveness of these drugs must thus be tested among vaccinated patients and against newer variants.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".