Regulators, Pivotal Clinical Trials, and Drug Regulation in the Age of COVID-19
Bibliographic record
Abstract
Medicine regulators rely on pivotal clinical trials to make decisions about approving a new drug, but little is known about how they judge whether pivotal trials justify the approval of new drugs. We explore this issue by looking at the positions of 3 major regulators: the European Medicines Agency, Food and Drug Administration, and Health Canada. Here we report their views and the implications of those views for the approval process. On various points, the 3 regulators are ambiguous, consistent, and demonstrate flexibility. The range of views may well reflect different regulatory cultures. Although clinical trial information from pivotal trials is becoming more available, regulators are still reluctant to provide detailed information about how that information is interpreted. As medicines and vaccines come up for approval for treatment of COVID-19, transparency in how pivotal trials are interpreted will be critical in determining how these treatments should be used.
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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 | 0.277 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.037 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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".