Relationship Between Sarcopenia and Both Physical Activity and Lifestyle in Patients With Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: Sarcopenia can affect the prognosis of patients with cirrhosis or hepatocellular carcinoma. Exercise therapy and nutritional therapy are carried out to prevent processing sarcopenia. In addition, changing lifestyle is also important. However, there are only few reports on the physical activities (PAs) and lifestyle of chronic liver disease patients and their association with sarcopenia. The aim of this study is to examine the relationship between sarcopenia in patients with chronic liver disease and both PA and lifestyle. METHODS: A total of 214 out-patients with chronic liver disease were enrolled into the present study. All patients were evaluated for with or without sarcopenia based on the sarcopenia diagnostic criteria of the Japan Society of Hepatology. Then, patient's characteristics and laboratory parameters were divided into two groups with or without sarcopenia and compared. In continuous variable with significant difference in univariate analysis, cut-off value was calculated by receiver operating characteristic curve. We determined which factors were associated with sarcopenia in univariate analyses, and variables significant in the univariate analyses were entered in a multivariable logistic regression model. RESULTS: ; P < 0.01), lower PA (6.6 (2.34 - 19.90) versus 16.5 (6.60 - 41.23) metabolic equivalents (METs)-h/week; P < 0.01) and longer total time sitting and lying on the day (7.43 ± 4.09 versus 5.68 ± 3.17 h/day; P = 0.01); retirement status (81.5% versus 48.1%; P < 0.01) and low frequency of driving (40% versus 20%; P = 0.01) were higher in sarcopenia patients than in non-sarcopenia patients. The independent predictive factors of sarcopenia, analyzed with logistic regression, were age (odds ratio (OR): 5.89, 95% confidence interval (CI): 2.15 - 16.20; P < 0.01), BMI (OR: 4.77, 95% CI: 1.87 - 12.10; P < 0.01) and PA (OR: 3.65, 95% CI: 2.15 - 16.20; P < 0.01). CONCLUSION: Sarcopenia patients' lifestyle characteristics were longer sedentary time and low frequency of driving, high retirement. Independent predictive factors of sarcopenia were elderly, low BMI and low PA. For these patients, intervention in the lifestyle for prevention of sarcopenia may be effective for patients with chronic liver disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".