Many trials of hydroxychloroquine for SARS-CoV-2 were redundant and potentially unethical: an analysis of the NIH clinical trials registry
Bibliographic record
Abstract
OBJECTIVE: We sought to map the landscape of trials investigating hydroxychloroquine (HCQ) for SARS-CoV-2 in order to draw conclusions about how clinical trials have been conducted in the pandemic environment and offer potential regulatory recommendations. STUDY DESIGN AND SETTING: We identified and captured data related to registered studies using HCQ to treat SARS-CoV-2 registered with the publicly available National Institutes of Health (NIH) Clinical Trials Registry between February and November 2020. RESULTS: Between February and November 2020, 206 studies investigating HCQ in SARS-CoV-2 were registered with the NIH Clinical Trials Registry. As of November 2020, 135 studies were listed as ongoing, 22 have been completed, and 46 are either suspended or have been terminated. Reasons for suspension or termination included difficulties with patient recruitment (n = 9), emerging evidence showing a lack of benefit of HCQ (n = 7), and recommendations by regulatory boards to discontinue (n = 10). CONCLUSION: Many clinical trials of HCQ were launched in the first months of the pandemic, and a significant proportion of them remained active as of November 2020. The medical community appears to have responded very quickly to political interest in HCQ, while responding much more slowly to the evolving medical evidence of its lack of efficacy.
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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 | 0.183 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".