Red Blood Cell Transfusion Practice Pattern Before and After Implementation of a Local Restrictive Transfusion Protocol in a Neonatal Intensive Care Unit
Bibliographic record
Abstract
Background A restrictive red blood cell (RBC) transfusion guideline was established in 2019 in a neonatal intensive care unit (NICU), suggesting a hemoglobin concentration threshold of 7 g/dL. Objectives (a) To determine its impact on the number of RBC transfusions and donor exposure; (b) to characterize RBC-transfusion determinants and justifications. Study Design Single-center retrospective historical control study comparing all neonates consecutively admitted to the NICU during two five-month periods: 401 patients in 2013 before and 402 patients in 2021 after restrictive guideline implementation. Possible determinants were assessed via logistic regressions and justifications via a questionnaire. Results In 2021, 9.2% of neonates received at least one RBC transfusion compared to 13.5% in 2013 ( p = .075). Adherence to protocol thresholds was 50%. Implementation of a restrictive transfusion protocol had some impact on determinants related to neonatal morbidity and illness severity and some impact on justifications being mainly based on hemoglobin value. Conclusion Our study demonstrates that the implementation of a restrictive RBC transfusion protocol tended to decrease transfusion rates and donor exposure in the NICU, but the trend was not statistically significant. Future work should focus on improving protocol adherence.
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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 | 0.006 |
| 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 | 0.000 |
| 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".