Assessing risk/benefit for trials using preclinical evidence: a proposal
Bibliographic record
Abstract
Moral evaluation of risk/benefit in early phase studies requires assessing the clinical promise of a candidate intervention using preclinical evidence. Yet, there is little to guide ethics committees, investigators, sponsors or other stakeholders morally charged with making these assessments ('evaluators'). In what follows, we draw on published guidelines for preclinical study design to develop a structured process for assessing the clinical promise of new interventions. In the first step, evaluators gather all relevant preclinical studies, assess the magnitude of treatment effects and determine clinical promise in light of various threats to valid clinical inference. In the second step, evaluators adjust the assessments of clinical promise from preclinical studies by examining how other agents in the same reference class-and supported by similar evidence-have fared in clinical development. Assessments of clinical promise can then be fed into the moral evaluation of risk and benefit in early phase trials. Though our approach has limitations, it offers a systematic and transparent method for assessing risk/benefit in early phase trials of novel interventions.
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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 | 0.615 | 0.638 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.029 | 0.057 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.077 | 0.059 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".