Clinical Overview of Sarcopenia, Frailty, and Malnutrition in Patients With Liver Cirrhosis
Bibliographic record
Abstract
Sarcopenia, frailty, and malnutrition in patients with liver cirrhosis are commonly observed and are associated with higher long-term mortality. Therefore, recognizing patients with increased nutritional risk and providing recommended interventions are essential in the long- and short-term management of cirrhosis, especially as alcoholic and non-alcoholic fatty liver disease continues to rise. Various assessment tools are available to gauge frailty and malnutrition but are infrequently used. Given the global burden of liver cirrhosis, periodic screening for malnutrition, sarcopenia, and frailty is desperately needed as it improves liver transplantation outcomes. Necessary steps include addressing knowledge gaps in professional healthcare workers and patients and using standardized assessment tools to counteract physical deconditioning as early as possible. One potential method for assessing sarcopenia involves using computed tomography to evaluate the skeletal muscle index. Regarding frailty, useful tools for longitudinal assessment include the liver frailty index and the Karnofsky performance status. Addressing educational requirements related to malnutrition involves seeking guidance from dieticians, who can provide counseling on achieving sufficient calorie and protein intake to combat the progression of malnutrition.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".