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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".