Efficacy and safety outcomes of proposed randomized controlled trials investigating hydroxychloroquine and chloroquine during the early stages of the COVID‐19 pandemic
Bibliographic record
Abstract
AIMS: To assess whether randomized clinical trials (RCTs) proposed to evaluate the treatment of patients with COVID-19 with hydroxychloroquine (HQ) or chloroquine early in the pandemic included plans to measure outcomes that would translate into meaningful efficacy/effectiveness and safety outcomes. METHODS: The World Health Organization International Clinical Trials Registry Platform database was searched for registers of RCTs evaluating HQ or chloroquine, alone or in combination, compared with other treatments for patients diagnosed with COVID-19. The final search was performed on 8 April 2020. RESULTS: Among 51 registered RCTs (median sample size 262; interquartile range: 100, 520), 34 (67%) reported a clinical outcome, 12 (24%) a surrogate outcome, and 5 (10%) a combination of clinical and surrogate outcomes as primary endpoints. Six (15%) trials included the World Health Organization scale for clinical improvement as a primary clinical outcome. Clinical improvement and mortality accounted for 45% of the unique domains among 18 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. CONCLUSION: The RCTs investigating HQ or chloroquine during the early stages of the COVID-19 pandemic included heterogeneous and insufficient approaches to measure efficacy/effectiveness and safety relevant to patients and clinical practice. These findings provide 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. Trialists need to adapt quickly to the research progress on COVID-19, ensuring that core outcome measures are assessed in ongoing RCTs.
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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 | 0.113 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| 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".