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 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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".