The relationship between skeletal muscle mass and exercise capacity in patients undergoing hemodialysis: An exploratory secondary‐analysis
Bibliographic record
Abstract
BACKGROUND: Protein-energy wasting is commonly observed in patients undergoing hemodialysis, leading to reduced muscle mass and, in severe cases, sarcopenia, which significantly impacts the patients' quality of life and clinical outcomes. Exercise interventions are frequently employed to address muscle loss in these patients. However, the relationship between exercise capacity and skeletal muscle mass in this context remains uncertain. In this study, researchers conducted a series of body composition analysis and exercise capacity test to explore the relationship between exercise capacity and skeletal muscle mass. METHODS: This study recruited 32 patients undergoing hemodialysis and analyzed their demographic and clinical parameters. To assess aerobic exercise capacity, the incremental shuttle walking test (ISWT) was conducted, while the handgrip strength test was used to evaluate muscle strength. The patients' body compositions were assessed using the Fresenius Body Composition Monitor, and the appendicular skeletal muscle was scanned using a GE Healthcare Lunar iDXA scanner. Three sequential linear models were employed to model the relationship between ISWT and skeletal muscle mass. Model 1 regressed ISWT against gender and age; Model 2 added handgrip strength, fat tissue index, and Davies comorbidity scores as additional predictors; and Model 3 further included the appendicular skeletal muscle index (appendicular skeletal muscle/height). FINDINGS: = 0.600). CONCLUSION: There was no significant correlation observed between exercise capacity and skeletal muscle mass in patients undergoing hemodialysis, indicating the factors influencing exercise capacity in this patient population are complex and that increasing skeletal muscle mass may not necessarily involve solely addressing the improvement of exercise capacity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".