Impact of Skeletal Muscle Mass Index, Intramuscular Adipose Tissue Content, and Visceral to Subcutaneous Adipose Tissue Area Ratio on Early Mortality of Living Donor Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle depletion has been shown to be an independent risk factor for poor survival in various diseases. However, in surgery, the significance of other body components including visceral and subcutaneous adipose tissue remains unclear. METHODS: This retrospective study included 250 adult patients undergoing living donor liver transplantation (LDLT) between January 2008 and April 2015. Using preoperative plain computed tomography imaging at the third lumbar vertebra level, skeletal muscle mass, muscle quality, and visceral adiposity were evaluated by the skeletal muscle mass index (SMI), intramuscular adipose tissue content (IMAC), and visceral to subcutaneous adipose tissue area ratio (VSR), respectively. The cutoff values of these parameters were determined for men and women separately using the data of 657 healthy donors for LDLT between 2005 and 2016. Impact of these parameters on outcomes after LDLT was analyzed. RESULTS: VSR was significantly correlated with patient age (P = 0.041), neutrophil-lymphocyte ratio (P < 0.001), body mass index (P < 0.001), and SMI (P = 0.001). The overall survival probability was significantly lower in patients with low SMI (P < 0.001), high IMAC (P < 0.001), and high VSR (P < 0.001) than in each respective normal group. On multivariate analysis, low SMI (hazard ratio [HR], 2.367, P = 0.002), high IMAC (HR, 2.096, P = 0.004), and high VSR (HR, 2.213, P = 0.003) were identified as independent risk factors for death after LDLT. CONCLUSIONS: Preoperative visceral adiposity, as well as low muscularity, was closely involved with posttransplant mortality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".