Risk Management for the 21st Century: Current Status and Future Needs
Bibliographic record
Abstract
Global adoption of risk management principles outlined in the International Conference on Harmonisation (ICH) E2E guideline and the Council for International Organizations of Medical Sciences (CIOMS) Working Group VI guidance introduced greater proactivity and consistency into the practice of pharmacovigilance and benefit-risk management throughout the lifecycle of a drug. However, following the release of these guidelines there have been important advances in the science and practice of risk minimisation itself, especially in terms of how risk minimisation measures (RMMs) are designed, implemented, disseminated and evaluated for effectiveness in real-world healthcare settings. In this article, we describe how the field of design, implementation, dissemination and evaluation of RMMs has advanced in recent years while highlighting current areas of challenge and possible solutions. Where possible we cite global examples to demonstrate how evidence-based approaches have informed the development of RMMs. In this context, while taking into consideration local healthcare system policies and national legislations, we conclude with a call for a global effort to harmonise certain areas that focus on, but are not limited to, standardising certain terms and definitions, consistent application of robust methodologies, and outline of best practices for risk minimisation design, implementation, and dissemination.
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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 | 0.095 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".