Platelet transfusion practice pattern before and after implementation of a local restrictive transfusion protocol in a neonatal intensive care unit
Bibliographic record
Abstract
BACKGROUND: platelets/L, a protocol supporting restrictive platelet transfusions was established in 2019 at the Sainte-Justine Hospital neonatal intensive care unit (NICU). This study aimed to: (1) determine the impact of a local restrictive transfusion protocol on the number of platelet transfusions and donor exposure; (2) compare platelet-transfusion determinants and justifications before and after its implementation. STUDY DESIGN AND METHODS: Prospective observational cohort chart-review study comparing all neonates consecutively admitted to the NICU during two 5-months periods: 2013 (before; N = 401) versus 2021 (after; N = 402). Possible determinants were assessed via logistic regressions and justifications via a questionnaire. RESULTS: Mean (± standard deviation) gestational age and birth weight were 34.9 ± 4.2 weeks and 2.5 ± 1.0 kg, respectively. In 2021, 5.0% were platelet-transfused versus 9.2% in 2013 (p = .027). Platelet transfusions decreased from a mean of 2.6 ± 1.7 in 2013 to 1.4 ± 0.7 in 2021 (p = .045). Adherence to protocol thresholds was of 70%. After protocol implementation, no infant received ≥4 platelet transfusions nor was exposed to ≥4 donors, compared to 29.7% and 21.6%, respectively, in 2013. Platelet transfusion justifications and determinants remained similar, except for severe intraventricular hemorrhage being an additional determinant in 2021. DISCUSSION: Restrictive local transfusion thresholds in a NICU decreased the proportion of platelet-transfused neonates by 46% and reduced donor exposure in transfused patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".