Pharmaceutical care in an inpatient pediatric intensive care unit: an international multicentric study
Bibliographic record
Abstract
Introduction Pediatric intensive care patient represent a population athigh risk for drug-related problems. Our objective is to describe drugrelated problems and intervention of four decentralized pharmacists inpediatric and cardiac intensive care unit.Materials & Methods Multicentric, descriptive and prospectivestudy over a six-month period (August 1st 2009-January 31st 2010).Drug-related problems and clinical interventions were compiled infour pediatric centers using a tool developed by the Socie´te´ Franc¸aisede Pharmacie Clinique. Data concerning patients, drugs, intervention,documentation, approval (if needed), and estimated impact werecompiled. The four pharmacists participating were from Belgium (B),France (F), Quebec (Q) and Switzerland (S).Results A total of 996 interventions were collected: 129 (13%) in B,238 (24%) in F, 278 (28%) in Q and 351 (35%) in S. These interventionstargeted 269 patients (median 22 month-old, 52% male): 69(26%) in B, 88 (33%) in F, 56 (21%) in Q and in S. These data werecollected during 28 non consecutive days in the clinical unit in B, 59days in F, 42 days in Q and 63 days in S. The main drug-relatedproblems were inappropriate administration technique (293, 29%),untreated indication (254, 25%) and supra therapeutic dosage (106,11%). The pharmacist's interventions concerned mainly administrationmode optimization (223, 22%), dose adjustment (200, 20%) andtherapeutic monitoring (164, 16%). The three major drug classesleading to interventions were anti-infectives for systemic use (233,23%) and alimentary tract and metabolism drugs (218, 22%). Interventionsconcerned mainly residents and all clinical staff (209, 21%).Among the 879 (88%) interventions requiring a physician's approval,731 (83%) were accepted. Interventions were considered as having amoderate (51%) or major (17%) clinical impact. Among the interventionsprovided, 10% were considered to have an economicalpositive impact. Differences and similarities between countries willbe presented at the poster session.Discussion & Conclusion Decentralized pharmacist at patient bedsideis a pre-requisite for pharmaceutical care. There are limitedstudies comparing the activity of clinical pharmacists betweencountries. This descriptive study illustrates the ability of clinicalpharmacist to identify and solve drug-related problems in pediatricintensive care unit in four different francophone countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".