Strategy for communicating benefit-risk decisions: a comparison of regulatory agencies' publicly available documents
Bibliographic record
Abstract
The assessment report formats of four major regulatory reference agencies, US Food and Drug Administration, European Medicines Agency, Health Canada, and Australia's Therapeutic Goods Administration were compared to a benefit-risk (BR) documentation template developed by the Centre for Innovation in Regulatory Science and a four-member Consortium on Benefit-Risk Assessment. A case study was also conducted using a US FDA Medical Review, the European Public Assessment Report and Australia's Public Assessment Report for the same product. Compared with the BR Template, existing regulatory report formats are inadequate regarding the listing of benefits and risks, the assigning of relative importance and values, visualization and the utilization of a detailed, systematic, standardized structure. The BR Template is based on the principles of BR assessment common to major regulatory agencies. Given that there are minimal differences among the existing regulatory report formats, it is timely to consider the feasibility of a universal template.
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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 | 0.284 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".