Assessing risk/benefit for trials using preclinical evidence: a proposal
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
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.
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 | 0.090 | 0.475 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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