ETHICS, AMBIGUITY AVERSION, AND THE REVIEW OF COMPLEX TRANSLATIONAL CLINICAL TRIALS
Bibliographic record
Abstract
Clinical trials of novel agents often present several layers of ethical challenge. Because time and resources for ethical and safety review are limited, how investigators, IRBs, and regulators allocate attention to a trial's various safety dimensions itself represents a critical ethical question. In what follows, I use the example of a Parkinson's disease gene transfer trial to show how risks involving unknown probabilities or outcomes (ambiguity), might sometimes draw attention away from risks that involve known probabilities or outcomes. This potentially undermines the goal of 'systematic and nonarbitrary analysis of risk' during ethical review. To counteract the possible effects of such attention biases, I propose that reviewers develop 'cognitive aids' like lists and, where appropriate, set aside time to discuss non-ambiguous risks. I also propose further research for addressing and understanding how attention allocation, emotion, and ambiguity influence ethical decision-making.
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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 | 0.350 | 0.600 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| 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".