OPNA, MAI, STOPP, START, maintenant GP-GP : Qui dit mieux?
Bibliographic record
Abstract
On ne nait pas vieux, on le devient, mais quand? Ceci n’est souvent perceptible que par un regard exterieur et non pas par le medecin traitant ou le patient qui se cotoient regulierement. Notons que la gestion de l’interaction de l’organisme du sujet âge avec ses medicaments devient un exercice de plus en plus complexe. L’etat de la fonction renale, les modifications journalieres cliniques de l’activite renale, les modifications de la vitesse du transit digestif, du pH, la modification de la masse musculaire, la modification de la masse grasse et maigre, de la clairance hepatique, de la denutrition et des cytochromes sont des modifications que nous ne faisons que commencer a percevoir. Cette complexite necessite une personnalisation de la pharmacotherapie, qui passe par une cooperation etroite entre medecin et pharmacien. A defaut, par manque de temps et de competences, ces parametres risquent de ne pas etre pris en consideration. Aucun algorithme, aucune recommandation ne pourra etre efficace (au sens du meilleur rapport avantagesrisques) et n’integrera les donnees galeniques, les horaires de prises, les interactions multiples, les intolerances et bien sur le suivi des progres de la pharmacie clinique.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".