ETHICS, AMBIGUITY AVERSION, AND THE REVIEW OF COMPLEX TRANSLATIONAL CLINICAL TRIALS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it