Remdesivir for coronavirus disease 2019 (COVID-19): a systematic review with meta-analysis and trial sequential analysis of randomized controlled trials
Bibliographic record
Abstract
Background In view of many unanswered clinical questions regarding treatment of COVID-19 with remdesivir, we systematically identified, critically appraised and summarized the findings from randomized controlled trials (RCTs) of remdesivir for COVID-19.Methods We searched relevant databases/websites (up to September 2020) and selected English-language RCT publications of remdesivir for COVID-19. We conducted meta-analysis using an inverse variance, random-effects model in addition to trial sequential analysis (TSA) for the efficacy outcomes: all-cause mortality, viral burden and clinical progression. Safety outcomes were diarrhoea, nausea, and vomiting. We calculated the relative risk (RR) and 95% confidence interval (CI) for all outcomes. Statistical heterogeneity was calculated using the I2 statistic.Results We included five RCTs (7540 participants) from 7237 citations. Most (80%) were of an unclear to high risk of bias. There was no evidence of a significant improvement with remdesivir (100 mg, 10 days) regarding all-cause mortality (RR 0.94, CI 0.82–1.07; I2 = 0%; 4 RCTs; 7143 patients), clinical progression (RR 1.08, CI 0.99–1.18; I2 = 70.4%; 3 RCTs; 1692 patients), or diarrhoea (RR 0.82, CI 0.40–1.66; I2 = 0%; 2 RCTs; 630 patients). Nausea occurred more often with remdesivir (RR 2.77, CI 1.28–6.03; I2 = 0%; 2 RCTs; 630 patients). TSA showed that the required information size was not reached for firm conclusions to be drawn.Conclusions and relevance There is insufficient evidence to support the use of remdesivir for treatment of COVID-19. More high-quality RCTs are needed for a stronger evidence. Until then, remdesivir should remain an experimental drug for COVID-19.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".