Clinical and novel insights into risk factors for sarcopenia in dialysis patients: a systematic review and meta-analysis
Bibliographic record
Abstract
We employed a meta-analysis to investigate the risk factors associated with sarcopenia in patients undergoing dialysis. We conducted a search in PubMed, Embase, Cochrane Library, and Web of Science databases. Inclusion criteria included case–control and cohort studies on risk factors for sarcopenia in dialysis patients. The search period spanned from the inception of each database to September 20, 2024. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS). Data analysis was performed using Stata 15.0. A total of 625 articles were screened, with 610 articles excluded based on predefined eligibility criteria, resulting in 15 articles involving 2904 individuals were included in the final analysis, meta-analysis results indicate that older dialysis patients [SMD = 0.76, 95% CI (0.54, 0.99), I 2 = 81%, P = 0.001], those with a lower BMI [SMD = -0.50, 95% CI (-0.80, -0.20), I 2 % = 87.4%, P = 0.02], a lower SMI [SMD = -2.67, 95% CI (-3.87, -1.47), I 2 % = 98.2%, P = 0.001], and those with diabetes [OR = 1.43, 95% CI (1.13, 1.82), I 2 % = 48.8%, P = 0.03] are more likely to develop sarcopenia. Based on current research, our study found that elderly dialysis patients, those with a lower BMI, lower SMI, and diabetic patients are more likely to develop sarcopenia. These findings highlight the necessity of early intervention for these high-risk groups. However, the study has limitations. Future research should address these limitations and investigate the mechanisms linking these risk factors to sarcopenia to develop targeted prevention and treatment strategies.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.050 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".