Transfusion premedication practices among pediatric health care practitioners in Canada: results of a national survey
Bibliographic record
Abstract
BACKGROUND: Although not supported by strong evidence, premedication (pretransfusion medication) is commonly prescribed to patients who have had a transfusion reaction. The research questions were: 1) What are Canadian pediatric practitioners' views and practices regarding premedication and 2) what are barriers to reducing premedication overuse in pediatrics? STUDY DESIGN AND METHODS: An online survey targeted hematology/oncology, emergency medicine, general surgery, intensive care, and cardiac intensive care practitioners in all 16 Canadian pediatric tertiary hospitals. The survey included four sections: demographic, clinical, future directions, and organizational questions. RESULTS: Fifty-five individuals from 15 of 16 pediatric tertiary care sites completed the survey: 53 physicians and two nurse practitioners. More than half of the respondents (55%; 30/55) were pediatric hematology/oncology providers, and 35% (19/55) were directors of their respective divisions. Eighty-seven percent of respondents estimated that they premedicate up to 25% of red blood cell (RBC) transfusions, and 13% premedicate 26% to 50% RBC transfusions. Proportions were similar for platelet transfusions. Most respondents reported that trainees are involved in transfusion and premedication order decisions. Seven percent believe that their hospital does not use leukoreduction and 27% are not sure. Sixty-five percent of respondents were not aware of a clinical practice guideline or a standard order set (SOS) at their institution: 51% are interested in having both available. Factors influencing the decision to premedicate and barriers to change were identified. CONCLUSION: Premedication practices are variable in Canadian pediatric academic hospitals. Evidence-based premedication clinical practice guidelines and SOS could be explored as a way to standardize practices. There were perceived educational and institutional barriers to practice change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".