Bibliographic record
Abstract
PURPOSE OF REVIEW: Cirrhosis is the result of the progression of necroinflammatory liver diseases leading to fibrosis, portal hypertension, and a catabolic state, which might cause muscle wasting or sarcopenia. In this review, we analyze the methods for muscularity assessment, the incidence and clinical impact of muscle wasting, and potential novel therapeutic strategies in cirrhosis. Finally, we evaluate the value of muscle wasting inclusion to conventional systems for liver transplant prioritization. RECENT FINDINGS: Muscle wasting is present in up to 45% of patients with cirrhosis and is associated with higher risk of sepsis-related death rather than liver failure mortality. Despite the fact that muscle wasting is not included in the scores for prognosis in cirrhotic patients, as in the case of Model for End-Stage Liver Disease (MELD) or Child-Pugh, its presence should alert clinicians to the same extent as other complications do, such as ascites, hepatic encephalopathy, or variceal bleeding. Two studies have shown increased mortality risk after liver transplantation in patients with muscle wasting, whereas one study did not. Modification of MELD to include muscle wasting is associated with a modest improvement in the prediction of mortality in patients with cirrhosis. SUMMARY: Muscle wasting is a frequent complication in cirrhosis and contributes to increased risk of sepsis-related mortality. The impact on mortality of muscle wasting after liver transplantation is controversial and needs further study. The MELD-sarcopenia score is associated with improvement in mortality prediction; however, prior to the widespread use of this composite score, validation in larger cohorts of patients with cirrhosis is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".