Comparing regulatory guidance on risk minimization/mitigation and the Reporting recommendation Intended for pharmaceutical Risk Minimization Evaluation Studies checklist
Bibliographic record
Abstract
The latest country-specific regulatory guidance for assessing effectiveness of risk minimization measures (RMM) strategies was identified across five continents-Africa (Egypt, South Africa), Asia (Australia, China, Japan, South Korea, Singapore), Europe (EU-27, United Kingdom), North America (Unites States, Canada) and South America (Brazil)-and compared to the Reporting recommendation Intended for pharmaceutical Risk Minimization Evaluation Studies (RIMES) checklist, developed to assess the quality of effectiveness evaluations and endorsed by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). RIMES checklist items address study hypothesis, participants, measures, statistical analysis and results. European Medical Agency (EMA) and Food and Drug Administration (FDA) guidance only partially aligned with RIMES, primarily for measures and results. In the absence of country-specific guidance, most countries recommended following EMA or FDA guidelines; Japan and South Africa mentioned the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH E2E) guideline; Brazil and China had no guidance/recommendations. Worldwide, there was a lack of RMM-specific guidance and, when guidance existed, they were not harmonized, and alignment with the RIMES checklist was limited.
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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 | 0.484 | 0.630 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".