Gestion des rappels et des retraits de médicaments en établissement de santé
Bibliographic record
Abstract
Resume Objectif : L’objectif de cet article est de decrire une demarche de mise a niveau de la gestion des retraits de lots de medicaments en etablissement de sante. Mise en contexte : La gestion des retraits de lots de medicaments est generalement manuelle et comporte plusieurs obstacles. Une bonne comprehension des obligations legales et professionnelles et une analyse de la situation peuvent aider a la mise en place d’une gestion plus structuree. Conclusion : Il existe peu de publications sur la gestion des retraits de lots de medicaments en etablissement de sante. Cet article propose une demarche structuree menant a la gestion en ligne des retraits et a l’interface de la base de donnees des retraits de medicaments avec les outils cliniques. Abstract Objective: The purpose of this article is to describe an approach to updating the management of recalled batches of medication in healthcare centers. Context : Management of recalled batches of medication is generally done manually and involves several obstacles. A good understanding of legal and professional obligations as well as an analysis of the situation can help establish a structured management approach. Conclusion : Very few publications address the management of recalled batches of medication in healthcare centers. This article proposes a structured approach leading to online management of recalled medication interfacing with a database of recalled medication and clinical tools. Key Words : Recall, medication, Health Canada.
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 | 0.015 | 0.066 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".