Pharmaceutical care in an inpatient pediatric hematopoietic stem cell transplant service
Bibliographic record
Abstract
INTRODUCTION: Hematopoietic stem cell transplant patients represent a population at high risk for drug-related problems. Our objective is to describe pharmacist interventions in a hematopoietic stem cell transplant pediatric unit. METHODS AND PATIENTS. The Hematopoietic Stem Cell Transplant Unit of the Centre Hospitalier Universitaire Sainte-Justine performs around 50 hematopoietic stem cell transplants per year. During a pharmaceutical care specialized residency program, a French pharmacist participated in certain clinical activities. Drug-related problems and clinical interventions were compiled over 31 nonconsecutive days using a tool developed by the Société Française de Pharmacie Clinique. Data concerning patients, drugs, intervention, documentation, approval (if needed), and estimated impact were compiled. RESULTS: During the 31-day period, 525 interventions were collected (16.9 +/- 3.7 per day), targeting 29 patients. The main drug-related problems were adverse drug reactions (N = 125, 23.8%), untreated indication (N = 92, 17.5%) and failure to receive drug (N = 89, 17.0%). The pharmacist's interventions concerned mainly dose adjustment (N = 174, 33.1%) and drug monitoring (N = 132, 25.1%). Among the 324 (61.7%) interventions requiring a physician's approval, 302 (93.2%) were accepted without any change. CONCLUSION: A pharmacist is able to perform clinically relevant interventions in a hematopoietic stem cell transplant unit, given the complexity of the pharmacotherapy. Our description of drug-related problems and interventions may help other pharmacists already working or developing pharmaceutical care in a hematopoietic stem cell transplant unit to compare their practice and it is one of the few reported in the literature.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".