Platelet Transfusion Practices Among Neonatologists in the United States and Canada: Results of a Survey
Bibliographic record
Abstract
OBJECTIVE: In the absence of scientific evidence, current neonatal platelet transfusion practices are based on physicians' preferences, expert advice, or consensus-driven recommendations. We hypothesized that there would be significant diversity in platelet transfusion triggers, product selection, and dosing among neonatologists in the United States and Canada. METHODS: A Web-based survey on neonatal platelet transfusion practices was distributed to all members of the American Academy of Pediatrics Perinatal Section in the United States and to all physicians listed in the 2005 Canadian Neonatology Directory. RESULTS: The overall response rate was 37% (1060 of 2875). In the United States, 37% (1007 of 2700) responded, of which 52% practiced at academic centers. Thirty percent (53 of 175) of Canadians responded, of whom 94% practiced at academic centers. As hypothesized, there was significant practice diversity in both countries. The survey also revealed that platelet transfusions are frequently administered to nonbleeding neonates with platelet counts of >50 x 10(9)/L. This practice is particularly prevalent among neonates with specific clinical conditions, including indomethacin treatment, preceding procedures, in the postoperative period, or with intraventricular hemorrhages. CONCLUSIONS: There is great variability in platelet transfusion practices among US and Canadian neonatologists, suggesting clinical equipoise in many clinical scenarios. Prospective randomized clinical trials to generate evidence-based neonatal platelet transfusion guidelines are needed.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".