Efficacy of Physical Activity in Prevention of Muscle Weakness in Patients With Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: Sarcopenia is a prognostic factor for patients with liver cirrhosis and hepatocellular carcinoma, and it affects the onset of hepatic encephalopathy. Therefore, the prevention of sarcopenia contributes to the improvement of the prognosis of patients with chronic liver disease (CLD). We focused on changes of hand grip strength (HGS), one of the indicators of sarcopenia. However, there are little data investigating the impact of physical activity (PA) on HGS in patients with CLD. This study aimed to clarify whether PA contributes to the prevention of muscle weakness in patients with CLD. METHODS: This was a prospective observational study. We examined the effect of PA on changes in HGS from the baseline to the endpoint in each group. Metabolic equivalents-hour/week (METs-h/w) was used to evaluate PA. In total, 183 outpatients with CLD were analyzed. We divided participants into four groups (low PA in younger patients (n = 20), high PA in younger patients (n = 33), low PA in elderly patients (n = 47), and high PA in elderly patients (n = 83)). RESULTS: Fifty-eight percent of patients were men, and the median (interquartile range) age was 69.0 (63.0, 75.0) years. The most common etiology of liver disease was hepatitis C (38%). The frequency of living alone and low exercise habit was significantly high, and sarcopenia was more obvious in elderly patients with low PA than in those with high PA. Additionally, the elderly with low PA showed significantly reduced HGS compared to that of the elderly with high PA (-1.00 (-2.27, 0.55) kg vs. 0.10 (-1.40, 1.10) kg, P < 0.05). However, changes in HGS in younger patients were not significant (-0.02 (1.83, 1.47) kg vs. 0.25 (-2.45, 2.05) kg, P = 0.96). Logistic regression analyses identified PA as the independent factor for prevention of decrease in HGS (odds ratio: 1.91, 95% confidence interval: 1.00 - 3.62, P = 0.049). CONCLUSIONS: Young patients with low PA were characterized by a long sedentary time; however, there was no loss of HGS. In contrast, elderly patients with CLD and low PA had significantly reduced HGS compared to that in elderly patients with CLD and high PA.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".