Derivation of A Risk Index for the Prediction of Massive Blood Transfusion in Liver Transplantation
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Bibliographic record
Abstract
Massive blood transfusion (MBT) remains a serious and common occurrence in liver transplantation surgery. This retrospective cohort study was undertaken to identify preoperative predictors of MBT and to develop a risk index for MBT in liver transplantation. Data were retrospectively collected on all liver transplantations carried out at a single institution between January 1998 and March 2004. Multivariable logistic regression analysis was used to identify independent predictor variables of MBT, defined as >/=6 units of red blood cell concentrate (RBC) in the first 24 hours of surgery. The model was internally validated by bootstrapping. Of the 460 liver transplant recipients, 193 (42%) received >/=6 units of RBC within 24 hours of surgery. Unadjusted analyses identified 12 preoperative predictors of MBT: age, height, gender, repeat transplantation, etiology of liver failure, and preoperative laboratory values (hemoglobin concentration, platelet count, international normalized ratio for prothrombin activity [INR], albumin, total bilirubin, and creatinine). In multivariable logistic regression, 7 independent predictors of MBT were identified: age (>40 years), hemoglobin concentration (</=10.0 g/dL), INR (1.2-1.99, and >2.0), platelet count (</=70 x 10(9)/L), creatinine (>/=110 micromol/L for female subjects and >/=120 micromol/L for male subjects), albumin (< 28 g/L), and repeat transplantation. The area under the receiver-operating characteristic curve (ROC) for the model was 0.82. By using the regression beta coefficients to derive weights for each of these predictors, a risk index was developed that assigned each patient a score between 0 and 8. The ROC for this risk index was 0.79. MBT in liver transplantation surgery can be accurately predicted by 7 readily available preoperative predictors.
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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 it