Prescription médicamenteuse inappropriée : les nouveaux critères STOPP/START
Bibliographic record
Abstract
La prescription médicamenteuse inappropriée (PMI) est un problème majeur de santé publique. Elle est associée à une augmentation de la morbi-mortalité et de la consommation des ressources de santé et ce principalement en raison de la survenue d’effets indésirables (EI). La révision systématique des prescriptions médicamenteuses est apparue depuis longtemps comme une solution pour limiter les PMI et les EI directement associés. Dans cet objectif, depuis 2008, la liste des critères STOPP/START est apparue comme un outil séduisant, logique, et facile d’utilisation.Cette version initiale vient d’être mise à jour. Après avoir détaillé les changements apportés, nous présentons le résultat de son adaptation en langue française par un groupe d’experts francophones belges, canadiens, français et suisses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".