Prescription électronique en établissement de santé : des résultats pas très convaincants
Bibliographic record
Abstract
Interventions : Implantation d’une prescription electronique dans un etablissement pediatrique en octobre 2002 afin d’assurer la gestion de toutes les ordonnances de medicaments des patients hospitalises et d’augmenter le respect des obligations reglementaires. Les tables du systeme ont ete prealablement validees par un groupe de medecins experts, incluant le recours a la liste locale de medicaments pour la plupart des medicaments prescrits. De plus, on a predetermine des algorithmes simples d’aide a la decision. Trois mois avant l’implantation, une campagne de sensibilisation sur la necessite de rapporter les evenements indesirables relies aux medicaments a ete menee. Au cours de l’etude, les ordonnances etaient redigees par des pediatres generalistes et specialistes ayant recu une formation de 2-3 heures sur la prescription electronique. Deux mille professionnels ont ete formes, incluant 250 medecins.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".