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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.798 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.071 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".