Safety and Efficacy of Remdesivir for the Treatment of COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the safety and efficacy of remdesivir in adult patients with COVID-19. METHODS: PubMed, Embase, Scopus, Web of Science, Cochrane Library, ClinicalTrials.gov, and medRxiv databases were searched using a search strategy tailored to each database. The Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the reporting of observational studies in epidemiology (STROBE) checklists were used for the studies' qualitative assessment. The outcomes studied were mortality, all adverse events, serious adverse events, and clinical improvement. The quantitative synthesis was conducted using fixed and random effects models in the CMA 2.2. Heterogeneity was tested using the I-squared (I2) measure. RESULTS: In general, six studies, including five randomized controlled trials and one cohort study were found eligible. Comparison of the findings related to both groups receiving remdesivir (10-day remdesivir group) and placebo/control group showed that remdesivir treatment had no significant effect on mortality at day 14 of the treatment (RR=0.769; 95% CI :0.563-1.050; p=0.098), and all adverse events (RR= 1.078; 95% CI: 0.908-1.279; p= 0.392). However, remdesivir had a significant effect on clinical improvement at day 14 compared to placebo/control (OR= 1.447; 95% CI: 1.005-2.085; p= 0.047) and reduced serious adverse events (RR= 0.736; 95% CI: 0.611-0.887; p= 0.001). CONCLUSION: Remdesivir has positive effects on clinical improvement, and reduction of the risk of serious adverse events. However, it does not influence the mortality at day 14 of treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.041 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".