A North American Expert Opinion Statement on Sarcopenia in Liver Transplantation
Bibliographic record
Abstract
Loss of muscle mass and function, or sarcopenia, is a common feature of cirrhosis and contributes significantly to morbidity and mortality in this population. Sarcopenia is a main indicator of adverse outcomes in this population, including poor quality of life, hepatic decompensation, mortality in patients with cirrhosis evaluated for liver transplantation (LT), longer hospital and intensive care unit stay, higher incidence of infection following LT, and higher overall health care cost. Although it is clear that muscle mass is an important predictor of LT outcomes, many questions remain, including the best modality for assessing muscle mass, the optimal cut‐off values for sarcopenia, the ideal timing and frequency of muscle mass assessment, and how to best incorporate the concept of sarcopenia into clinical decision making. For these reasons, we assembled a group of experts to form the North American Working Group on Sarcopenia in Liver Transplantation to use evidence from the medical literature to address these outstanding questions regarding sarcopenia in LT. We believe sarcopenia assessment should be considered in all patients with cirrhosis evaluated for liver transplantation. Skeletal muscle index (SMI) assessed by computed tomography constitutes the best‐studied technique for assessing sarcopenia in patients with cirrhosis. Cut‐off values for sarcopenia, defined as SMI < 50 cm 2 /m 2 in male and < 39 cm 2 /m 2 in female patients, constitute the validated definition for sarcopenia in patients with cirrhosis. Conclusion: The management of sarcopenia requires a multipronged approach including nutrition, exercise, and additional pharmacological therapy as deemed necessary. Future studies should evaluate whether recovery of sarcopenia with nutritional management in combination with an exercise program is sustainable as well as how improvement in muscle mass might be associated with improvement in clinical 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".