Myosteatosis in Cirrhosis: A Review of Diagnosis, Pathophysiological Mechanisms and Potential Interventions
Bibliographic record
Abstract
Myosteatosis, or pathological excess fat accumulation in muscle, has been widely defined as a lower mean skeletal muscle radiodensity on computed tomography (CT). It is reported in more than half of patients with cirrhosis, and preliminary studies have shown a possible association with reduced survival and increased risk of portal hypertension complications. Despite the clinical implications in cirrhosis, a standardized definition for myosteatosis has not yet been established. Currently, little data exist on the mechanisms by which excess lipid accumulates within the muscle in individuals with cirrhosis. Hyperammonemia may play an important role in the pathophysiology of myosteatosis in this setting. Insulin resistance, impaired mitochondrial oxidative phosphorylation, diminished lipid oxidation in muscle and age-related differentiation of muscle stem cells into adipocytes have been also been suggested as potential mechanisms contributing to myosteatosis. The metabolic consequence of ammonia-lowering treatments and omega-3 polyunsaturated fatty acids in reversing myosteatosis in cirrhosis remains uncertain. Factors including the population of interest, design and sample size, single/combined treatment, dosing and duration of treatment are important considerations for future trials aiming to prevent or treat myosteatosis in individuals with cirrhosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".