Bibliographic record
Abstract
Les statistiques américaines dévoilent que les réactions adverses aux médicaments surviennent en moyenne chez un patient sur 15. Chaque année, environ 2,2 millions de personnes sont touchées par des effets délétères reliés aux médicaments. De ce nombre, les interactions sont imputables dans le tiers des cas et comptent pour la moitié des coûts de santé qui en découlent. On estime que l’incidence d’interactions médicamenteuses est aussi basse que 3 à 5 % pour les personnes consommant peu de médicaments et aussi élevée que 20 à 100 % pour les personnes hospitalisées prenant entre 10 et 20 entités médicamenteuses. Bien que des constellations d’interactions médicamenteuses soient possibles et surviennent, le nombre actuel d’effets adverses manifestes et graves résultant de telles associations est plutôt modeste. Le prescripteur doit savoir anticiper ces interactions en tenant compte des médicaments prescrits, des médicaments en vente libre, des facteurs environnementaux et des co-morbidités.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".