What do we talk about when we talk about “equipoise”? Stakeholder interviews assessing the use of equipoise in clinical research ethics
Bibliographic record
Abstract
Abstract Introduction Equipoise, generally defined as uncertainty about the relative effects of the treatments being compared in a trial, is frequently referenced as an ethical standard for the conduct of randomized clinical trials. However, it seems to be defined in several different ways and may be used differently by different individuals. We explored how clinical researchers, chairs of research ethics boards, and philosophers of science define and reason with this term. Methods We completed semi-structured interviews about clinical trial ethics with 15 clinical researchers, 15 research ethics board chairs, and 15 philosophers of science/bioethicists. Each participant was asked a standardized set of 10 questions, 4 of which were specifically about equipoise. All interviews were conducted telephonically and transcribed. Responses were grouped and analysed via a modified grounded theory method. Results Forty-three respondents defined equipoise in 7 logically distinct ways, and 2 respondents could not explicitly define it. The most common definition, offered by 14 respondents (31%), defined “equipoise” as a disagreement at the level of a community of physicians. There was significant variability in definitions offered between and within groups. When asked how they would “operationalize” equipoise — i.e. check or test for its presence — respondents provided 7 alternatives, the most common being in relation to a literature review (15/45, 33%). The vast majority of respondents (35/45, 78%) felt the concept was helpful, though many acknowledged that the lack of a clear definition or operationalization was problematic. Conclusion There is significant variation in definitions of equipoise offered by respondents, suggesting that parties within groups and between groups may be referring to different concepts when they reference “equipoise”. This non-uniformity may impact fairness and transparency and opens the door to potential ethical problems in the evaluation of clinical trials — for instance, a patient may understand equipoise very differently than the researchers enrolling her in a trial, which could cause her agreement to participate to be based upon false premises.
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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 | 0.197 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".