The efficacy and safety of hydroxychloroquine for COVID-19 prophylaxis: A systematic review and meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND: Populations such as healthcare workers (HCW) that are unable to practice physical distancing are at high risk of acquiring Coronavirus disease-2019 (COVID-19). In these cases pharmacological prophylaxis would be a solution to reduce severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) transmission. Hydroxychloroquine has in vitro antiviral properties against SARS CoV-2. We therefore sought to determine the efficacy and safety of hydroxychloroquine as prophylaxis for COVID-19. METHODS AND FINDINGS: We electronically searched EMBASE, MEDLINE, the Cochrane COVID-19 Register of Controlled Trials, Epistemonikos COVID-19, clinicaltrials.gov, and the World Health Organization International Clinical Trials Registry Platform up to September 28th, 2020 for randomized controlled trials (RCTs). We calculated pooled relative risks (RRs) for dichotomous outcomes with the corresponding 95% confidence intervals (CIs) using a random-effect model. We identified four RCTs (n = 4921) that met our eligibility criteria. The use of hydroxychloroquine, compared to placebo, did not reduce the risks of developing COVID-19 (RR 0.82, 95% CI 0.65 to 1.04, moderate certainty), hospitalization (RR 0.72, 95% CI 0.34 to 1.50, moderate certainty), or mortality (RR 3.26, 95% CI 0.13 to 79.74, low certainty), however, hydroxychloroquine use increased the risk of adverse events (RR 2.76, 95% CI 1.38 to 5.55, moderate certainty). CONCLUSION: Although pharmacologic prophylaxis is an attractive preventive strategy against COVID-19, the current body of evidence failed to show clinical benefit for prophylactic hydroxychloroquine and showed a higher risk of adverse events when compared to placebo or no prophylaxis.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".