Chloroquine and Hydroxychloroquine for COVID-19: Demonstrating the Importance of the Scientific Process
Bibliographic record
Abstract
Although chloroquine and hydroxychloroquine have been touted in the media as “miracle drugs” in the fight against COVID-19, the research backing this claim is controversial. Some studies have shown impressive results – like one study that reported a 100% cure rate – while numerous other studies have reported inefficacy. However, the evidence presented in many of these studies has been laden with glaring flaws – from low sample sizes to a lack of control group – and many had been pre-printed without peer review. No matter how contentious the evidence for efficacy may be, studies have shown an undeniable association with serious adverse events, most notably heart arrythmias. In this article, we will discuss where the hype originated, the current state of evidence, and where the future of these in drugs is headed in the current climate of 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 | 0.173 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".