Bibliographic record
Abstract
The most commonly recognized complications in cirrhotic patients include ascites, hepatic encephalopathy, variceal bleeding, susceptibility for infections, kidney dysfunction, and hepatocellular carcinoma; however, severe muscle wasting or sarcopenia are the most common and frequently unseen complications which negatively impact survival, quality of life, and response to stressor, such as infections and surgeries. At present, D'Amico stage classification, Child-Pugh, and MELD scores constitute the best tools to predict mortality in patients with cirrhosis; however, one of their main limitations is the lack of assessing the nutritional and functional status. Currently, numerous methods are available to evaluate the nutrition status of the cirrhotic patient; nevertheless, most of these techniques have limitations primarily because lack of objectivity, reproducibility, and prognosis discrimination. In this regard, an objective and reproducible technique, such as muscle mass quantification with cross-sectional imaging studies (computed tomography scan or magnetic resonance imaging) constitute an attractive index of nutritional status in cirrhosis. Sarcopenia is part of the frailty complex present in cirrhotic patients, resulting from cumulative declines across multiple physiologic systems and characterized by impaired functional capacity, decreased reserve, resistance to stressors, and predisposition to poor outcomes. In this review, we discuss the current accepted and new methods to evaluate prognosis in cirrhosis. Also, we analyze the current knowledge regarding incidence and clinical impact of malnutrition and sarcopenia in patients with cirrhosis and their impact after liver transplantation. Finally, we discuss existing and potential novel therapeutic approaches for malnutrition in cirrhosis, emphasizing the recognition of sarcopenia in an effort to reduced morbidity related and improved survival in cirrhosis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".