Platelet transfusion practices in neonatology: A single-center observational study
Bibliographic record
Abstract
Aims: Platelet transfusions are common in the neonatal intensive care unit (NICU), yet practices vary substantially. This study aims to determine platelet transfusion incidence, determinants, and justifications in neonatology. Methods: Single-center prospective cohort study, including all patients consecutively admitted to the CHU Sainte-Justine Hospital NICU over a 5-month period in 2013. Data were collected by chart review and transfusion justifications were assessed using a questionnaire. Results: A total of 401 participants were included. Mean birth weight (BW) was 2.34±1.01 kg and gestational age (GA) was 34.4±4.5 weeks. Thirty-seven neonates (9.2%) received at least one platelet transfusion. Platelet-transfused neonates were mostly extremely preterm (40.5%) or term (24.3%). The median pre-platelet transfusion count was 57 × 109/L (9–285 × 109/L). Compared to non-transfused patients, those who received at least one platelet transfusion had a significantly lower BW and GA, higher CRIB-II and SNAPPE-II scores (all p and#60;0.001) and were more frequently admitted for respiratory disease (p and#60;0.001), hypoxic-ischemic encephalopathy (p=0.009), and hemolytic disease of the newborn (p and#60;0.001). Gestational age and#60;28 weeks (pand#60;0.001), mechanical ventilation requirements (p=0.008), and platelet nadir ≤150 × 109/L (pand#60;0.001) upon admission were independently associated with a higher risk of platelet transfusion in this cohort. Most frequent justifications for ordering a first platelet transfusion were low platelet counts (86.5%), underlying disease (78.4%) and illness severity (37.8%). Conclusion: Pre-transfusion platelet counts in neonates varied widely and were higher than the thresholds proposed in the literature. Several factors other than platelet count predicted risk of platelet transfusion in this cohort.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".