Derivation of A Risk Index for the Prediction of Massive Blood Transfusion in Liver Transplantation
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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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".