Hydroxychloroquine for treatment of non‐hospitalized adults with <scp>COVID</scp>‐19: A meta‐analysis of individual participant data of randomized trials
Bibliographic record
Abstract
Hydroxychloroquine (HCQ) was initially promoted as an oral therapy for early treatment of coronavirus disease 2019 (COVID-19). Conventional meta-analyses cannot fully address the heterogeneity of different designs and outcomes of randomized controlled trials (RCTs) assessing the efficacy of HCQ in outpatients with mild COVID-19. We conducted a pooled analysis of individual participant data from RCTs that evaluated the effect of HCQ on hospitalization and viral load reduction in outpatients with confirmed COVID-19. We evaluated the overall treatment group effect by log-likelihood ratio test (-2LL) from a generalized linear mixed model to accommodate correlated longitudinal binary data. The analysis included data from 11 RCTs. The outcome of virological effect, assessed in 1560 participants (N = 795 HCQ, N = 765 control), did not differ significantly between the two treatment groups (-2LL = 7.66; p = 0.18) when adjusting for cohort, duration of symptoms, and comorbidities. The decline in polymerase chain reaction positive tests from day 1 to 7 was 42.0 and 41.6 percentage points in the HCQ and control groups, respectively. Among the 2037 participants evaluable for hospitalization (N = 1058 HCQ, N = 979 control), we found no significant differences in hospitalization rate between participants receiving HCQ and controls (odds ratio 0.995; 95% confidence interval 0.614-1.610; -2LL = 0.0; p = 0.98) when adjusting for cohort, duration of symptoms, and comorbidities. This individual participant data meta-analysis of 11 HCQ trials that evaluated severe acute respiratory syndrome-coronavirus 2 viral clearance and COVID-19 hospitalization did not show a clinical benefit of HCQ. Our meta-analysis provides evidence to support the interruption in the use of HCQ in mild COVID-19 outpatients to reduce progression to severe disease.
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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 | 0.031 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".