Association of preoperative parameters with postoperative mortality and long-term survival after liver transplantation
Bibliographic record
Abstract
BACKGROUND: The ability of Child-Turcotte-Pugh (CTP) or Model for End-Stage Liver Disease (MELD) scores to predict recipient survival after liver transplantation is controversial. This analysis aims to identify preoperative parameters that might be associated with early postoperative mortality and long-term survival after liver transplantation. METHODS: We studied a total of 15 parameters, using both univariate and multivariate models, among adults who underwent primary liver transplantation. RESULTS: A total of 458 primary adult liver transplants were performed. Fifty-seven (12.44%) patients died during the first 3 postoperative months and composed the early mortality group. The remaining 401 patients composed the long-term patient survival group. The parameters that were identified through univariate analysis to be associated with early postoperative mortality were CTP score, MELD score, bilirubin, creatinine, international normalized ratio and warm ischemia time (WIT). In all multivariate models, WIT retained its statistical significance. The 10-year long-term survival was 65%. The parameters that were identified to be independent predictors of long-term survival were the recipient's sex (improved survival in women, p = 0.005), diagnosis of hepatocellular cancer (p=0.015) and recipient's age (p=0.024). CONCLUSION: Either CTP or MELD score, in conjunction with WIT, might have a role in predicting early postoperative mortality after liver transplantation, whereas the recipient's sex and the absence of hepatocellular cancer are associated with improved long-term survival.
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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".