Therapeutic Emergency Use Authorizations (EUAs) During Pandemics: Double-edged Swords
Bibliographic record
Abstract
Given the urgent need for treatments during the coronavirus disease 2019 pandemic, the US Food and Drug Administration issued emergency use authorizations (EUAs) for multiple therapies. In several instances, however, these EUAs were issued before sufficient evidence of a given therapy's efficacy and safety were available, potentially promoting ineffective or even harmful therapies and undermining the generation of definitive evidence. We describe the strengths and weaknesses of the different therapeutic EUAs issued during this pandemic. We also contrast them to the vaccine EUAs and suggest a framework and criteria for an evidence-based, trustworthy, and publicly transparent therapeutic EUA process for future pandemics.
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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 | 0.598 | 0.756 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.030 | 0.039 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".