Sarcopenic obesity in cirrhosis—The confluence of 2 prognostic titans
Bibliographic record
Abstract
Sarcopenia and obesity are 2 major health conditions with a growing prevalence in cirrhosis. The concordance of these 2 conditions, sarcopenic obesity, is associated with higher rates of mortality and impact on the metabolic profile and physical function than either condition alone. To date, there is little consensus surrounding the diagnostic criteria for sarcopenia, obesity or as a result, sarcopenic obesity in patients with cirrhosis. Cross-sectional imaging, although the most accurate diagnostic technique, has practical limitations for routine use in clinical practice. Management strategies are focused on increasing muscle mass and strength. The present review provides an overview of the diagnosis, pathophysiology, prognostic implications and management strategies available for sarcopenic obesity in cirrhosis. We also discuss the associated condition myosteatosis, the pathological accumulation of fat in skeletal muscle. Much work needs to be done to advance both clinical care and research in this area. Future directions require consensus definitions for sarcopenia, obesity and sarcopenic obesity, an expansion of our understanding of the complex pathogenesis of the muscle-liver-adipose tissue axis in cirrhosis and evidence to support management recommendations for nutrition, exercise and pharmacological therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".