Development and Validation of a Predictive Risk Score for Blood Transfusion in Patients Undergoing Curative‐Intent Surgery for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Among patients undergoing liver resection for intrahepatic cholangiocarcinoma (ICC), perioperative bleeding requiring blood transfusion is a common complication, yet preoperative identification of patients at risk for transfusion remains challenging. The objective of this study was to develop a preoperative risk score for blood transfusion requirement during surgery for ICC. METHODS: Patients undergoing curative-intent liver surgery for ICC (1990-2020) were identified from a multi-institutional database. A predictive model was developed and validated. An easy-to-use risk calculator was made available online. RESULTS: Among 1420 patients, 300 (21.1%) received an intraoperative transfusion. Independent predictors of transfusion included severe preoperative anemia (OR = 1.65, 95% CI 1.10-2.47), T2 category or higher (OR = 2.00, 95% CI 1.36-3.02), positive lymph nodes (OR = 1.75, 95% CI 1.32-2.32) and major resection (OR = 2.56, 95%CI 1.85-3.58). Receipt of blood transfusion significantly correlated with worse outcomes. The model showed good discriminative ability in both training (AUC = 0.68, 95% CI 0.66-0.72) and bootstrapping validation (C-index = 0.67, 95% CI 0.65-0.70) cohorts. An online risk calculator of blood transfusion requirement was developed (https://catalano-giovanni.shinyapps.io/TransfusionRisk). CONCLUSIONS: Intraoperative blood transfusion was significantly associated with poor postoperative outcomes among patients undergoing surgery for ICC. The identification of patients at high risk of transfusion could improve perioperative patient care and blood resources allocation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".