Intégration d’une base de données de produits de santé naturels à un progiciel pharmacie en établissement de santé
Bibliographic record
Abstract
ObjectifL’objectif de cet article est de presenter les modalites d’integration d’une base de donnees de produits de sante naturels (PSN) a un progiciel pharmacie en etablissement de sante.Materiels et methodesIl s’agit d’une etude descriptive avec analyse de faisabilite menee au sein du CHU Sainte-Justine, Montreal, QC, Canada. Dans un premier temps, deux bases de donnees ont ete comparees (Natural Standard ® et Natural Medicines Comprehensive Database). Les donnees de la base retenue ont dans un deuxieme temps ete inclues dans des pages web.ResultatsA partir d’une revue documentaire et d’une evaluation des deux bases de donnees en ligne, nous avons retenu la base Natural Standard ®. Nous avons developpe un programme en asp.net interface a l’intranet de la pharmacie. Un total de 53 895 couples d’interactions et de 1 512 denominations communes de medicaments ont ete importes de Natural Standard ®. Un total de 1 765 couples de denominations communes de Natural Standard ® et de GesPhaRx ® (logiciel de gestion des dossiers therapeutiques des patients) ont ete saisies manuellement.ConclusionIl existe peu de donnees en ce qui concerne l’integration de bases de donnees de produits de sante naturels a des livrets therapeutiques informatises. Cette etude de faisabilite decrit le concept permettant l’integration de pres de 60 000 couples d’interactions medicaments-PSN sous format web en temps reel en etablissement de sante.
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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".