Fallibility: A New Perspective on the Ethics of Clinical Trial Enrollment
Bibliographic record
Abstract
The ethical principle of 'equipoise', introduced in 1974, represents the most widely influential justification for the enrollment of patients into randomized clinical trials. However, definitions of equipoise vary, and its terms are not universally accepted. In this paper, we suggest a new way of approaching the ethics of clinical trial enrollment, which we call fallibility. The principle of fallibility argues that all physician opinions are sufficiently uncertain to warrant investigation, and that the ethical justification for any trial becomes a question of its epistemic validity, by which we mean the strength of its hypotheses and methods. The principle of fallibility can be translated into practice through the virtues of humility, skepticism and caring. While we cite recent examples from stroke medicine to demonstrate the limitations of equipoise, we propose that fallibility may offer a more general means of addressing the controversies that arise surrounding randomized controlled trials in many disciplines of medicine.
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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 | 0.305 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.091 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.023 | 0.038 |
| 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".