Mortality, viral clearance, and other clinical outcomes of hydroxychloroquine in COVID‐19 patients: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Many meta‐analyses have been published about the efficacy of hydroxychloroquine (HCQ) in coronavirus disease 2019 (COVID‐19). Most of them included observational studies, and few have assessed HCQ as a prophylaxis or evaluated its safety profile. We searched multiple databases and preprint servers for randomized controlled trials (RCTs) that assessed HCQ for the treatment or prevention of COVID‐19. We summarized the effect of HCQ on mortality, viral clearance, and other clinical outcomes. Out of 768 papers screened, 21 RCTs with a total of 14,138 patients were included. A total of 9 inpatient and 3 outpatient RCTs assessed mortality in 8596 patients with a pooled risk difference of 0.01 (95% confidence interval [CI] 0.00–0.03, I 2 = 1%, p = 0.07). Six studies assessed viral clearance at 7 days with a pooled risk ratio (RR) of 1.11 (95% CI 0.86–1.42, I 2 = 61%, p = 0.44) and 5 studies at 14 days with a pooled RR of 0.96 (95% CI 0.89–1.04, I 2 = 0%, p = 0.34). Several trials showed no significant effect of HCQ on other clinical outcomes and. Five prevention RCTs with 5012 patients found no effect of HCQ on the risk of acquiring COVID‐19. Thirteen trials showed that HCQ was associated with increased risk of adverse events. We observed, with high level of certainty of evidence, that HCQ is not effective in reducing mortality in patients with COVID‐19. Lower certainty evidence also suggests that HCQ neither improves viral clearance and other clinical outcomes, nor prevents COVID‐19 infection in patients with high‐risk exposure. HCQ is associated with an increased rate of adverse events.
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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 | 0.088 | 0.360 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.055 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".