Update on efficacy of the approved remdesivir regimen for treatment of COVID-19: a systematic review with meta-analysis and trial sequential analysis of randomized controlled trials
Bibliographic record
Abstract
Background Efficacy of remdesivir for COVID-19 remains unclear. We updated our published systematic review to better inform on the use of remdesivir for COVID-19.Methods We searched for randomized controlled trials (RCTs) among hospitalized COVID-19 patients. Meta-analysis was conducted using an inverse variance, random-effects model, presenting relative risk (RR) or mean difference (MD) and their associated 95% confidence intervals (CIs). Statistical heterogeneity was calculated using the I2 statistic. In addition, we conducted trial sequential analysis (TSA). Outcomes with additional data were clinical progression, hospitalization days, and all-cause mortality.Results We included nine RCTs (12,876 individuals). Three trials each were of a low, unclear, and a high risk of bias. Compared with no treatment/placebo, remdesivir (100 mg daily, over 10 days) significantly improved clinical progression (RR 1.06, CI 1.02–1.11), but did not significantly reduce hospitalization days (MD −0.48, CI −2.18–1.21) and all-cause mortality (RR 0.92, CI 0.84–1.01). TSA suggested that further information is not required to conclude on the efficacy of remdesivir in improving clinical progression, and that, while more information is required for hospitalization days and all-cause mortality, further RCTs to prove fewer hospitalization days may be futile, as efficacy of remdesivir for this outcome is unlikely.Conclusions Remdesivir appeared promising for COVID-19, but there is insufficient evidence of its efficacy. High quality 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 | 0.080 | 0.593 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.058 | 0.019 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| 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; 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".