The Effect of Hemodialysis Treatment on Sarcopenia in Patients Newly Starting Hemodialysis
Bibliographic record
Abstract
BACKGROUND: The research aimed to establish sarcopenia occurrence rates among patients beginning hemodialysis treatment and assess muscle mass and function changes and sarcopenia status after six months of dialysis. METHODS: The prospective observational study included 110 patients who were new to hemodialysis (maximum 1 month). Patients were grouped as sarcopenic (n = 33) and non-sarcopenic (n = 77) according to EWGSOP2 criteria. Demographic characteristics, body composition by bioelectrical impedance analysis, hand grip strength, walking speed tests, and laboratory parameters were evaluated at baseline and at 6 months. RESULTS: in the sarcopenic group (p = 0.65), but this difference was not significant when compared with the non-sarcopenic group (p = 0.32). Walking speed time improved from 10.5 ± 3.8 s to 8.7 ± 3.3 s in the sarcopenic group and the difference between the groups was statistically significant (p = 0.03). Right hand grip strength increased from 20.8 ± 9.2 kg to 31.2 ± 7.8 kg (p = 0.68). In multivariate analysis, SMI (OR: 0.38, p < 0.001) was significantly associated with sarcopenia, while age (p = 0.37), gender (p = 0.15), albumin (p = 0.62), and CRP (p = 0.82) were not significantly associated. CONCLUSIONS: Patients who start hemodialysis have high rates of sarcopenia but have significant muscle mass and functional improvements during the first six months of effective hemodialysis treatment. The removal of uremic toxins produces beneficial effects on muscle metabolism according to these results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".