Clinical studies assessing the efficacy, effectiveness and safety of remdesivir in management of COVID‐19: A scoping review
Bibliographic record
Abstract
AIMS: Remdesivir is 1 of the repurposed drugs under investigation to treat patients with COVID-19. Clinicians and decision-makers need a summary of the most recent evidence. This scoping review maps the evidence on the efficacy, effectiveness and safety of remdesivir for patients with COVID-19, up to 14 September 2020. METHODS: Our scoping review searched Pubmed, Embase (Ovid), Scopus and 17 primary trial registries for empirical publications or active registered clinical trials for data on the efficacy, effectiveness, or safety of remdesivir for COVID-19 or SARS-CoV-2. We conducted a narrative synthesis of the included publications. RESULTS: Seventeen empirical studies and 23 clinical trial registrations (n = 40) accumulated 46 508 participants. We found 4 published randomized-controlled trials accumulating 2293 patients. Two trials reported shorter median recovery time and better clinical status among patients who received remdesivir compared with the control groups. Observational studies report an association between remdesivir treatment and decreased mortality, as well as increased survival. The most common adverse reaction was hepatic impairment, although the trials reported a similar proportion of adverse events in the intervention and control groups. CONCLUSION: Remdesivir might shorten the time to clinical improvement among hospitalized adults with severe COVID-19. Trial data report a similar proportion of adverse events in treated and control groups. The results of the 23 registered active trials, including more than 30 000 participants, will shed light on the efficacy and safety of the antiviral. The findings of the remaining clinical trials expected to report results in 2020 will allow a quantitative synthesis of available evidence.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".