Antiviral drugs for coronavirus disease 2019 (COVID-19): a systematic review with network meta-analysis
Bibliographic record
Abstract
BACKGROUND: To better inform clinical practice, we summarized the findings from randomized controlled trials (RCTs) of antivirals for COVID-19. METHODS: We systematically searched for literature up to September 2020, and included English-language publications of RCTs among hospitalized COVID-19 patients. We conducted network meta-analysis combining results of both the direct and indirect comparisons of interventions. The efficacy outcomes were clinical progression, all-cause mortality, and viral clearance, and safety outcomes were diarrhea, nausea, and vomiting. We generated treatment rankings (best to worst) and summarized rank probabilities using rankogram. RESULTS: We included 15 RCTs (14,418 patients) from 7,237 retrieved citations. There was no evidence for efficacy of the assessed antivirals compared with placebo/no treatment or with another antiviral for all efficacy outcomes. Lopinavir (400 mg)/ritonavir (100 mg) significantly increased diarrhea, nausea, and vomiting compared with placebo/no treatment and other antivirals, and was ranked worst for these outcomes, while triazavirin (250 mg), baloxavir marboxil (80 mg), and remdesivir (100 mg - 10 days) ranked best, respectively. CONCLUSIONS AND RELEVANCE: The available evidence does not support the use of any antiviral drugs for COVID-19. Cautious interpretations of the findings are, however, advised considering the paucity of the evidence. More RCTs are needed for a stronger evidence base.
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How this classification was reachedexpand
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.039 | 0.022 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".