Model competencies in regulatory therapeutic product assessment: Health Canada's good review guiding principles as a reviewing community's code of intellectual conduct
Bibliographic record
Abstract
PURPOSE: This article describes some work from the Therapeutic Products Directorate of Health Canada regarding Good Review Practices (GRP). METHODS AND RESULTS: Background information is provided on the Therapeutic Products Directorate (TPD) and its regulatory activities regarding drug and medical device assessment in both the pre- and post-market setting. The TPD Good Review Guiding Principles (GRGP) are described which include a Definition of a Good Therapeutic Product Regulatory Review, Ten Hallmarks of a Good Therapeutic Product Regulatory Review and Ten Precepts. Analysis of the guiding principles discusses possible linkages between the guiding principles and intellectual virtues. CONCLUSIONS: Through this analysis an hypothesis is developed that the guiding principles outline a code of intellectual conduct for Health Canada's reviewers of evidence for efficacy, safety, manufacturing quality and benefit-risk regarding therapeutic products. Opportunities to advance therapeutic product regulatory review as a scientific discipline in its own right and to acknowledge that these reviewers constitute a specific community of practice are discussed. Integration of intellectual and ethical approaches across therapeutic product review sectors is also suggested.
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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 | 0.228 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.018 | 0.065 |
| Scholarly communication | 0.030 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".