AI-based body composition score predicts survival after liver transplantation
Bibliographic record
Abstract
PURPOSE: Body composition has a significant role to predict survival in patients with malignant disease. This study evaluates the importance of body composition for predicting short- and long-term survival after liver transplantation. Additionally, the sex specific differences will be evaluated. METHODS: Body composition, of all patients who underwent liver transplantation between January 2011 and December 2023 with computed tomography prior liver transplantation, was assessed fully automated with AI based technique. Pre-, intra- and postoperative data were retrospectively reported. Uni- and multivariate regression analyses was performed to identify independent prognostic factors for survival. The statistical analyses was performed separately for male and female with comparison of the both groups. RESULTS: There were 346 patients (60.1%male, 39.9%female) with median age of 52.2 years (SD 10.8) included to the study. The univariate and multivariate cox regression analyses have identified the ratio of the subcutaneous fat volume to muscle volume as well as the ratio of the visceral fat volume to muscle volume as significant prognostic parameter for the overall survival. The separate analyses of the two groups show that these factors predict survival in male and female. However, visceral fat and also the ratio of FVM is significantly higher in male. CONCLUSION: Based on the results of our study we can conclude that the ratio of visceral fat volume to muscle volume (FVM-ratio) has an essential impact on overall survival after liver transplantation in male and female patients. The fully automated AI based assessment is fast, accurate and investigator independent.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".