Fluvoxamine for the Early Treatment of COVID-19: A Meta-analysis of Randomized Clinical Trials
Bibliographic record
Abstract
Fluvoxamine is widely prescribed as an antidepressant. Recent studies show the drug may have a clinical benefit in treating COVID-19. We aimed to perform a meta-analysis of the existing randomized trials of fluvoxamine compared with placebo on the early treatment of COVID-19 patients. We included only randomized clinical trials enrolling ambulatory patients with early-stage disease (symptoms < 7 days) for the prevention of hospitalization. We searched MEDLINE and clinicaltrials.gov databases to identify trials and extract data with clarifications from the study investigators. We performed a fixed-effects meta-analysis and sensitivity analyses via R to evaluate the pooled estimate of hospitalization. We included three randomized trials: STOP COVID 1 and 2, and the TOGETHER Trial. The studies included a total of 2,196 patients. The STOP COVID trials measured clinical deterioration whereas the TOGETHER Trial measured hospitalization as the primary outcome. All trials reported on hospitalization up to day 28. The meta-analysis results show that patients receiving fluvoxamine were 31% less likely to experience clinical deterioration or hospitalization compared with placebo (risk ratio, 0.69; 95% CI, 0.54-0.88). A sensitivity analysis using the definition of hospitalization resulted in a risk reduction of 21% (95% CI, 0.60-1.03). Data from three randomized controlled trials show that fluvoxamine was associated with a reduction in the primary outcome measure (either clinical deterioration or composite outcome of hospitalization or extended emergency setting observation), although analysis of hospitalization-only was not statistically significant. More evidence from future trials is still needed to support the findings of this meta-analysis.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.066 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".