Deploying the Precautionary Principle to Protect Vulnerable Populations in Canadian Post-Market Drug Surveillance
Bibliographic record
Abstract
Drug regulatory bodies aim to ensure that patients have access to safe and effective drugs; however, no matter the quality of pre-licensure studies, uncertainty will remain regarding the safety and effectiveness of newly approved drugs until a large and diverse population uses those drugs. Recent analyses of Canada’s post-market drug surveillance (PMDS) system have found that Canada’s PMDS system requires strengthening and that efforts must be improved to monitor and address the safety and effectiveness of approved drugs among vulnerable populations. Given the uncertainty that exists when drugs enter the market, some have suggested that the precautionary principle is relevant to guiding decision-making in this context. This paper responds to recommendations that the Canadian PMDS system should be responsive to the health needs of vulnerable populations by assessing the utility of deploying the precautionary principle to guide a post-market strategy for vulnerable populations.
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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 | 0.074 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".