Renal dysfunction independently predicts muscle mass loss in patients following liver transplantation
Bibliographic record
Abstract
BACKGROUND: Liver transplantation (LT) is the only curative treatment for cirrhosis. However, the presence of complications can impact outcomes following LT. Sarcopenia, or muscle mass loss, is highly prevalent in patients with cirrhosis and is associated with longer hospitalization stays and a higher infection rate post-surgery. We aimed to identify patients at higher risk of early sarcopenia post-LT. METHODS: This retrospective study included 79 cirrhotic patients who underwent LT. Muscle mass was evaluated using the third lumbar spine vertebra skeletal muscle index (SMI) and sarcopenia was defined using established cut-off values. Computerized tomography (CT) scans performed within a six-month peri-operative period (three months pre- and post-LT) were included in the study. Complications and comorbidities were collected and correlated to SMI post-LT and predictive models for SMI post-LT were constructed. RESULTS: The overall prevalence of sarcopenia was 46% and 62% before and after LT, respectively. Newly developed sarcopenia was found in 42% of patients. Post-LT sarcopenia was associated with longer hospital stays (54±37 versus 29±10 days, p = 0.002), higher number of infection (3±1 versus 1±2, p = 0.027), and greater number of complications (5±2 versus 3±2, p < 0.001) compared to absence of sarcopenia. Multivariate analyses showed that the SMI post-LT was independently associated with pre-LT renal function markers, the glomerular filtration rate (GFR) and creatinine (Model 1, GFR: β = 0.33; 95% CI 0.04–0.17; p = 0.003; Model 2, Creatinine: β = –0.29; 95% CI –0.10 to –0.02; p = 0.009). CONCLUSIONS: The present study highlights the potential role of renal dysfunction in the development and persistence of sarcopenia after LT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".