Morphological, Electrophysiological, and Metabolic Characteristics of Skeletal Muscle in People with End-Stage Renal Disease: A Critical Review
Bibliographic record
Abstract
PURPOSE: Fatigue is one of the most frequent debilitating symptoms reported by people with end-stage renal disease (ESRD) on haemodialysis (HD) therapy. A wide range of underlying abnormalities, including skeletal muscle weakness, have been implicated as causes of this fatigue. Skeletal muscle weakness is well established in this population, and such muscle weakness is amenable to physical therapy treatment. The purpose of this review was to identify morphological, electrophysiological, and metabolic characteristics of skeletal muscles in people with ESRD/HD that may cause skeletal muscle weakness. METHOD: Electronic databases were searched for relevant literature from inception to March 2010. Inclusion criteria were English language; adult subjects with ESRD/HD; and the use of muscle biopsy, electromyography, and nuclear magnetic spectroscopy ((31)P-NMRS) techniques to evaluate muscle characteristics. RESULTS: In total, 38 studies were included. All studies of morphological characteristics reported type II fibre atrophy. Electrophysiological characteristics included both neuropathic and myopathic skeletal muscle changes. Studies of metabolic characteristics revealed higher cytosolic inorganic phosphate levels and reduced effective muscle mass. CONCLUSION: The results indicate an array of changes in the morphological, electrophysiological, and metabolic characteristics of skeletal muscle structure in people with ESRD/HD that may lead to muscle weakness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".