A Quality Improvement Project to Limit Perioperative Transfusion During Craniofacial Surgery in Infants and Children
Bibliographic record
Abstract
Introduction: Open surgery for craniosynostosis in infants and children is frequently associated with significant intraoperative blood loss and the need for allogeneic transfusion therapy. A lack of consensus at our institution led to inconsistent transfusion therapy during open cranial vault remodeling. We sought to address this through a multidisciplinary quality improvement approach to decrease variability in transfusion practices. Methods: A streamlined intraoperative transfusion algorithm, from the moment the patient was evaluated in the preoperative holding area on the day of surgery through their departure from the postanesthesia care unit, was implemented during quarter 1 of 2021. We aimed to decrease the percentage of intraoperative transfusions administered at hemoglobin (Hgb) levels greater than 7.5 g/dL in stable patients without massive bleeding undergoing open craniosynostosis surgery from 40% in January 2021 to 0% by December 2023. Results: From January 2018 to December 2023, 118 pediatric patients underwent open cranial vault remodeling for craniosynostosis. Before the implementation of the protocol in quarter 1 of 2021, open cranial vault reconstruction (CVR) intraoperative transfusion rates in cases where the threshold was not met (Hgb >7.5 g/dL) were 32%. After protocol implementation, open CVR intraoperative transfusion rates that did not meet the threshold averaged 5%. Conclusions: In this quality improvement project, we decreased transfusion rates for patients with Hgb greater than 7.5 g/dL from 32% to 5% during 3 years. We have successfully developed and implemented an intraoperative blood transfusion algorithm that is generalizable and requires minimal human and material resources.
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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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".