Severe muscle depletion predicts postoperative length of stay but is not associated with survival after liver transplantation
Bibliographic record
Abstract
Muscle depletion or sarcopenia is associated with increased mortality in patients with cirrhosis; how it affects mortality after liver transplantation requires further study. In this study, we aimed to establish whether sarcopenia predicts increased morbidity or mortality after liver transplantation. We analyzed 248 patients with cirrhosis who had a computed tomography (CT) scan including the third lumbar vertebra before liver transplantation. Data were recovered from medical charts, the skeletal muscle cross-sectional area was measured with CT, and sarcopenia was defined with previously published sex- and body mass index-specific cutoffs. One hundred sixty-nine patients (68%) were male, and the mean age at transplantation was 55 ± 1 years. The etiologies of cirrhosis were hepatitis C virus (51%), alcohol (19%), autoimmune liver diseases (15%), hepatitis B virus (8%), and other etiologies (7%). Sarcopenia was present in 112 patients (45%), and it was more frequent in males (P = 0.002), patients with ascites (P = 0.02), and patients with higher bilirubin levels (P = 0.05), creatinine levels (P = 0.02), international normalized ratios (P = 0.04), Child-Pugh scores (P = 0.002), and Model for End-Stage Liver Disease scores (P = 0.002). The median survival period after liver transplantation was 117 ± 17 months for sarcopenic patients and 146 ± 20 months for nonsarcopenic patients (P = 0.4). Sarcopenic patients had longer hospital stays (40 ± 4 versus 25 ± 3 days; P = 0.005) and a higher frequency of bacterial infections within the first 90 days after liver transplantation (26% versus 15%, P = 0.04) in comparison with nonsarcopenic patients. In conclusion, sarcopenia is one of the most common complications in patients with cirrhosis and is predictive of longer hospital stays and a higher risk of perioperative bacterial infections after liver transplantation, but it is not associated with increased 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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".