Bibliographic record
Abstract
Resume Objectif : Decrire le processus d’implantation d’un systeme de pompes intelligentes pour l’administration des medicaments intraveineux a l’Hopital general juif de Montreal. Contexte : L’administration des medicaments par voie parenterale constitue une source importante d’erreurs graves, dont certaines pourraient etre evitees. Couplee a une base de donnees, la pompe intelligente permet au personnel soignant de respecter les limites de debit preetablies et specifiques au medicament et a l’unite de soins. L’implantation d’un tel systeme necessite un effort concerte de plusieurs services d’un hopital, dont le departement de pharmacie. Conclusion : Le pharmacien est un des acteurs incontournables dans l’usage approprie des medicaments intraveineux administres au moyen de pompes intelligentes. La gestion et l’analyse des rapports d’alertes, ainsi que l’optimisation de la compliance a la base de donnees, font partie de l’amelioration constante de la qualite des soins. Abstract Objective: To describe the process of implementing smart pumps for the administration of intravenous medications at the Montreal Jewish General Hospital. Context: Parenteral administration of medication is a significant source of serious medication errors, many of which can be avoided. Coupled to a database, smart pumps allow healthcare providers to respect the limits of pre-established rates specific to a drug and to a ward. The implementation of such a system requires a concerted effort from many hospital services, one being the pharmacy department. Conclusion: The pharmacist has a major role in the appropriate use of intravenous medications administered by smart pumps. Management and analysis of alarm reports as well as optimization of database compliance are part of a continuous improvement in the quality of care Key words: smart pumps; infusion pumps; intravenous administration; Plum A+TD; MedNet MedsTD
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".