Incidence and predictors of massive bleeding in children undergoing liver transplantation: A single‐center retrospective analysis
Bibliographic record
Abstract
Summary Background Liver transplantation represents a major surgery involving a highly vascular organ. Reports defining the scope of bleeding in pediatric liver transplants are few. Aims We conducted a retrospective analysis of liver transplants performed at our pediatric tertiary care center to quantify blood loss, blood product utilization, and to determine predictors for massive intraoperative bleeding. Methods Pediatric patients who underwent isolated liver transplantation at Boston Children's Hospital between 2011 and 2016 were included. The amount of blood product transfused in the perioperative period and the incidence of postoperative complications were reported. Univariable and multivariable logistic regressions were used to determine predictors for massive bleeding, defined as estimated blood loss exceeding one circulating blood volume within 24 hours. Results Sixty‐eight children underwent liver transplantation during the study period and were included in the analysis. Multivariable logistic regression analysis identified the following independent predictors of massive bleeding: preoperative hemoglobin level <8.5 g/ dL ( OR 11.09, 95% CI 1.87‐65.76), INR >1.5 ( OR 11.62, 95% CI 2.36‐57.26), platelet count <100 10 9 /L ( OR 7.92, 95% CI 1.46‐43.05), and surgery duration >600 minutes ( OR 6.97, 95% CI 0.99‐48.92). Conclusions Pediatric liver transplantation is associated with substantial blood loss and a significant blood product transfusion burden. A 43% incidence of massive bleeding is reported. Further efforts are needed to improve bleeding management in this high‐risk population.
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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 | 0.000 | 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 | 0.000 |
| 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".