Are Phase 1 Trials Therapeutic? Risk, Ethics, and Division of Labor
Bibliographic record
Abstract
Despite their crucial role in the translation of pre-clinical research into new clinical applications, phase 1 trials involving patients continue to prompt ethical debate. At the heart of the controversy is the question of whether risks of administering experimental drugs are therapeutically justified. We suggest that prior attempts to address this question have been muddled, in part because it cannot be answered adequately without first attending to the way labor is divided in managing risk in clinical trials. In what follows, we approach the question of therapeutic justification for phase 1 trials from the viewpoint of five different stakeholders: the drug regulatory authority, the IRB, the clinical investigator, the referring physician, and the patient. Our analysis shows that the question of therapeutic justification actually raises multiple questions corresponding to the roles and responsibilities of the different stakeholders involved. By attending to these contextual differences, we provide more coherent guidance for the ethical negotiation of risk in phase 1 trials involving patients. We close by discussing the implications of our argument for various perennial controversies in phase 1 trial practice.
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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 | 0.399 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".