Chloroquine/ hydroxychloroquine prevention of coronavirus disease (COVID-19) in the healthcare setting; protocol for a randomised, placebo-controlled prophylaxis study (COPCOV)
Bibliographic record
Abstract
There is no proven preventative therapy or vaccine against COVID-19. Theinfection has spread rapidly and there has already been a substantial adverse impact on the global economy. Healthcare workers have been affected disproportionately in the continuing pandemic. Significant infection rates in this critical group have resulted in a breakdown of health services in some countries. Chloroquine, and the closely related hydroxychloroquine, are safe and well tolerated medications which can be given for years without adverse effects. Chloroquine and hydroxychloroquine have significant antiviral activity against SARS-CoV-2, and despite the lack of benefit of hydroxychloroquine treatment in patients hospitalised with severe COVID-19, these drugs could still work in prevention. The emerging infection paradigm of an early viral peak, and late inflammation where there is benefit from corticosteroids. If these direct actiing antivirals are to work, they have the best chance given either early in infection and before infection occurs. We describe the study protocol for a multi-centre, multi-country randomised, double blind, placebo controlled trial to answer the question- can chloroquine/ hydroxychloroquine prevent COVID-19. 40,000 participants working in healthcare facilities or involved in the management of COVID-19 will be randomised 1:1 to receive chloroquine/ hydroxychloroquine or matched placebo as daily prophylaxis for three months. The primary objective is the prevention of symptomatic, virological or serologically proven coronavirus disease (COVID-19). The study could detect a 23% reduction from an incidence of 3% in the placebo group for either drug with 80% power. Secondary objectives are to determine if chloroquine/hydroxychloroquine prophylaxis attenuates severity, prevents asymptomaticCOVID-19 and symptomatic acute respiratory infections of another aetiology (non-SARS-CoV-2).
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.016 |
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".