Efficacy and safety outcomes in randomized controlled trials investigating hydroxychloroquine for COVID-19
Bibliographic record
Abstract
Aims: To assess whether randomized clinical trials (RCTs) proposed to evaluate treatment of COVID-19 with HQ or chloroquine include outcome definitions and data collection plans to produce meaningful efficacy/effectiveness and safety outcomes. Methods: We searched the World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP) database for registers of RCTs evaluating HQ or chloroquine, alone or in any combination, to treat patients diagnosed with COVID-19 compared with any other treatment option. The final search was performed on April 8th, 2020. Results: Among 51 registered RCTs (median sample size of 262; IQR: 100, 520), 34 (67%) reported a clinical outcome, 12 (24%) a surrogate outcome, and five (10%) a combination of clinical and surrogate outcomes as primary endpoints. Clinical status/recovery and all-cause mortality/mortality accounted for 49% of the unique domains among 20 different clinical outcome domains of efficacy. Twenty-four (47%) RCTs did not describe plans to assess safety outcomes; when assessed, safety outcomes were determined in generic terms of total, severe or serious adverse events. Conclusions: The RCTs investigating HQ or chloroquine include heterogenous and insufficient approaches to measure efficacy/effectiveness and safety that are relevant to patients and clinical practice. These findings provide important insights to inform clinical and regulatory decisions that can be drawn about the efficacy/effectiveness and safety of these agents in patients with COVID-19.
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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 | 0.186 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".