Ethical Considerations for Phase I Trials in Oncology
Bibliographic record
Abstract
Phase I trials often represent the first occasion where new cancer strategies are tested in patients. Various developments in cancer biology, methodology, regulation, and medical ethics have altered the ethical landscape of such trials. We provide a narrative review of contemporary ethical challenges in design, conduct, and reporting of phase I cancer trials and outline recommendations for addressing each. We organized our review around four topics, supplementing the first three with scoping reviews: (1) benefit/risk, (2) research biopsies, (3) therapeutic misconception and misestimation, and (4) reporting. The main ethical challenges of conducting phase I trials stem from three issues. First, phase I trials often involve higher research burden and scientific uncertainty compared with other cancer trials. Second, many patients arrive at phase I trials at a transitional point in their illness trajectory where they have exhausted standard survival-extending options. Third, phase I trial results play a major role in informing downstream drug development and regulatory decisions. Together, these issues create distinct pressures for study design, ethical review, informed consent, and reporting. Developments in methodology, regulation, cancer biology, and ethical awareness have helped mitigate some of these challenges, while introducing others. We conclude our review with a series of recommendations regarding trial design, ethical review, consent, and reporting. We also outline several unresolved questions that, if addressed, would strengthen the ethical foundation of phase I cancer trials.
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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 | 0.248 | 0.438 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.017 |
| 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; 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".