Skeletal Muscle Pathological Fat Infiltration (Myosteatosis) Is Associated with Higher Mortality in Patients with Cirrhosis
Bibliographic record
Abstract
Myosteatosis (pathological fat accumulation in muscle) is defined by lower mean skeletal muscle radiodensity in CT. We aimed to determine the optimal cut-offs for myosteatosis in a cohort of 855 patients with cirrhosis. CT images were used to determine the skeletal muscle radiodensity expressed as Hounsfield Unit (HU). Patients with muscle radiodensity values below the lowest tertile were considered to have myosteatosis. Competing-risk analysis was performed to determine associations between muscle radiodensity and pre-transplant mortality. Muscle radiodensity less than 33 and 28 HU in males and females, respectively, were used as cut-offs to identify myosteatosis. In the univariate analysis, cirrhosis etiology, MELD score, refractory ascites, variceal bleeding, hepatic encephalopathy, sarcopenia and myosteatosis were predictors of mortality. Myosteatosis association with mortality remained significant after adjusting for confounding factors (sHR 1.47, 95% CI 1.17−1.84, p = 0.001). Patients with concurrent presence of myosteatosis and sarcopenia constituted 17% of the patient population. The cumulative incidence of mortality was the highest in patients with concomitant sarcopenia and myosteatosis (sHR 2.22, 95% CI 1.64−3.00, p < 0.001). In conclusion, myosteatosis is common in patients with cirrhosis and is associated with increased mortality. The concomitant presence of myosteatosis and sarcopenia is associated with worse outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".