Efficacy of antiviral therapies for COVID-19: a systematic review of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to pose a significant threat to public health worldwide. The purpose of this study was to review current evidence obtained from randomized clinical trials on the efficacy of antivirals for COVID-19 treatment. METHODS: A systematic literature search was performed using PubMed to identify randomized controlled trials published up to September 4, 2021 that examined the efficacy of antivirals for COVID-19 treatment. Studies that were not randomized controlled trials or that did not include treatment of COVID-19 with approved antivirals were excluded. Risk of bias was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) method. Due to study heterogeneity, inferential statistics were not performed and data were expressed as descriptive statistics. RESULTS: Of the 2,284 articles retrieved, 31 (12,440 patients) articles were included. Overall, antivirals were more effective when administered early in the disease course. No antiviral treatment demonstrated efficacy at reducing COVID-19 mortality. Sofosbuvir/daclatasvir results suggested clinical improvement, although statistical power was low. Remdesivir exhibited efficacy in reducing time to recovery, but results were inconsistent across trials. CONCLUSIONS: Although select antivirals have exhibited efficacy to improve clinical outcomes in COVID-19 patients, none demonstrated efficacy in reducing mortality. Larger RCTs are needed to conclusively establish efficacy.
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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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".