Efficacy and safety of antiviral treatment for COVID-19 from evidence in studies of SARS-CoV-2 and other acute viral infections: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antiviral medications are being given empirically to some patients with coronavirus disease 2019 (COVID-19). To support the development of a COVID-19 management guideline, we conducted a systematic review that addressed the benefits and harms of 7 antiviral treatments for COVID-19. METHODS: We searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), PubMed and 3 Chinese databases (CNKI, WANFANG and SinoMed) through Apr. 19, medRxiv and Chinaxiv through Apr. 27, and Chongqing VIP through Apr. 30, 2020. We included studies of ribavirin, chloroquine, hydroxychloroquine, umifenovir (arbidol), favipravir, interferon and lopinavir/ritonavir. If direct evidence from COVID-19 studies was not available, we included indirect evidence from studies of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for efficacy outcomes and other acute respiratory viral infections for safety outcomes. RESULTS: In patients with nonsevere COVID-19 illness, the death rate was extremely low, precluding an important effect on mortality. We found only very low-quality evidence with little or no suggestion of benefit for most treatments and outcomes in both nonsevere and severe COVID-19. An exception was treatment with lopinavir/ritonavir, for which we found low-quality evidence for a decrease in length of stay in the intensive care unit (risk difference 5 d shorter, 95% confidence interval [CI] 0 to 9 d) and hospital stay (risk difference 1 d shorter, 95% CI 0 to 2 d). For safety outcomes, evidence was of low or very low quality, with the exception of treatment with lopinavir/ritonavir for which moderate-quality evidence suggested likely increases in diarrhea, nausea and vomiting. INTERPRETATION: To date, persuasive evidence of important benefit in COVID-19 does not exist for any antiviral treatments, although for each treatment evidence has not excluded important benefit. Additional randomized controlled trials involving patients with COVID-19 will be needed before such treatments can be administered with confidence.
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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 | 0.005 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".