Effectiveness of remdesivir for the treatment of hospitalized COVID‐19 persons: A network meta‐analysis
Bibliographic record
Abstract
Several randomized clinical trials (RCTs) that investigated the effectiveness of remdesivir for the treatment of coronavirus disease-2019 (COVID-19) have generated inconsistent evidence. The present study aimed to synthesize available RCT evidence using network meta-analyses (NMAs). Both blinded and open-label RCTs in PubMed database from inception to 7 June 2020 that contained "remdesivir", "Covid-19", and "trial" in the abstracts conducted on hospitalized COVID-19 persons were identified and screened. The studies must have at least one remdesivir arm and evaluated one of the pre-specified outcomes. The outcomes were clinical improvement between days 10 to 15 after randomization and clinical recovery during the follow-up period. The identified literature was supplemented with relatively recent studies that were known to the researchers if not already included. Frequentist NMAs with random effects were conducted. Both 10-day and 5-day remdesivir regimens were associated with higher odds of clinical improvement (odds ratio [OR] of 10-day regimen: 1.35, 95% confidence interval [CI], 1.09-1.67); OR of 5-day regimen: 1.81, 95% CI, 1.32-2.45, and higher probabilities of clinical recovery (relative risk [RR] of 10-day regimen: 1.24, 95% CI, 1.07-1.43; RR of 5-day regimen: 1.47, 95% CI, 1.16-1.87 compared with placebo. Remdesivir may have clinical benefits among hospitalized COVID-19 persons.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".