Cartographie des termes décrivant les produits utilisés en pratique pharmaceutique
Bibliographic record
Abstract
Resume Objectif : Encadrer le bon usage des termes de produits utilises en pratique pharmaceutique a l’aide d’une cartographie de ces termes. Mise en contexte : En consultant les differents sites web gouvernementaux et d’autres organismes, on note qu’il n’y a pas de cartographie detaillee situant l’ensemble des produits et des termes utilises ainsi que leur relation hierarchique. Resultats : Au total, 42 termes lies a des produits utilises en pratique pharmaceutique ont ete retenus pour la realisation de la cartographie. Quatre-vingt-un pour cent (34/42) sont definis par un organisme de competence federale contre 12 % (5/42) par un organisme de competence provinciale. Nous avons ete en mesure d’estimer le nombre de produits applicables a un groupe de termes dans seulement 33 % des cas (14/42). Au total, ces termes decoulent de lois (26 %, 11/42), de reglements (40 %, 17/42) ou d’autres textes (31 %, 13/42). La revue comporte une cartographie synthese et une annexe detaillee constituees de chaque groupe de termes en francais et en anglais, de quelques exemples de produits utilises en pratique pharmaceutique, de la definition commentee et, lorsqu’elle existe, d’une estimation du nombre de produits sur le marche canadien. Conclusion : Cette revue documentaire propose une cartographie de 42 termes decrivant les produits utilises en pratique pharmaceutique. Cette cartographie aide a situer l’ensemble des termes, leur hierarchie et peut contribuer a un meilleur encadrement de leur utilisation en pratique pharmaceutique. Le pharmacien ou l’assistant technique capable de comprendre correctement chacun de ces termes sera davantage en mesure de respecter les balises juridiques et normatives applicables a ces denominations. Abstract Objective: To guide the proper use of terms for products used in pharmaceutical practice by mapping these terms. Background: Upon consulting the various government websites and the websites of different organizations, we note that there is no detailed map situating all the products and terms used and their hierarchical relationship. Results: A total of 42 terms pertaining to products used in pharmaceutical practice were selected for the mapping. Eighty-one percent (34/42) are defined by a body under federal jurisdiction versus 12% (5/42) by a body under provincial jurisdiction. We were able to estimate the number of products applicable to a group of terms in only 33% of the cases (14/42). These terms are from statutes (26%, 11/42), regulations (40%, 17/42) and other texts (31%, 13/42). The review includes a summary map and a detailed appendix consisting of each group of terms in French and English, a few examples of products used in pharmaceutical practice, the definitions with commentary and, where available, an estimate of the number of products on the Canadian market. Conclusion: This literature review led to a map of 42 terms for products used in pharmaceutical practice. This map helps situate all the terms and shows their hierarchy, It can contribute to better guidance for their use in pharmaceutical practice. The pharmacist or technical assistant with a proper understanding of all these terms will be better able to comply with the legal and normative guidelines applicable to these names.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".