The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries
Bibliographic record
Abstract
PURPOSE: The aim of the study was to develop a European list of potentially inappropriate medications (PIM) for older people, which can be used for the analysis and comparison of prescribing patterns across European countries and for clinical practice. METHODS: A preliminary PIM list was developed, based on the German PRISCUS list of potentially inappropriate medications and other PIM lists from the USA, Canada and France. Thirty experts on geriatric prescribing from Estonia, Finland, France, the Netherlands, Spain and Sweden participated; eight experts performed a structured expansion of the list, suggesting further medications; twenty-seven experts participated in a two-round Delphi survey assessing the appropriateness of drugs and suggesting dose adjustments and therapeutic alternatives. Finally, twelve experts completed a brief final survey to decide upon issues requiring further consensus. RESULTS: Experts reached a consensus that 282 chemical substances or drug classes from 34 therapeutic groups are PIM for older people; some PIM are restricted to a certain dose or duration of use. The PIM list contains suggestions for dose adjustments and therapeutic alternatives. CONCLUSIONS: The European Union (EU)(7)-PIM list is a screening tool, developed with participation of experts from seven European countries, that allows identification and comparison of PIM prescribing patterns for older people across European countries. It can also be used as a guide in clinical practice, although it does not substitute the decision-making process of individualised prescribing for older people. Further research is needed to investigate the feasibility and applicability and, finally, the clinical benefits of the newly developed list.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".