Extending Clinical Equipoise to Phase 1 Trials Involving Patients: Unresolved Problems
Bibliographic record
Abstract
Notwithstanding requirements for scientific/social value and risk/benefit proportionality in major research ethics policies, there are no widely accepted standards for these judgments in Phase 1 trials. This paper examines whether the principle of clinical equipoise can be used as a standard for assessing the ratio of risk to direct-benefit presented by drugs administered in one category of Phase 1 study--first-in-human trials involving patients. On the basis of the supporting evidence for, and architecture of, Phase 1 studies, the articles offers two provisional conclusions: (1) the risks of drug administration in such trials cannot generally be justified on therapeutic grounds but by appeal to the social value of the research; and (2) a framework for adjudicating the ratio of risk/social-value must be developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.770 | 0.793 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.062 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.020 | 0.036 |
| 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".