The Prevalence of Metabolic Syndrome and Factors Associated with Quality of Dialysis among Hemodialysis Patients in Southern Taiwan
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to evaluate the prevalence of metabolic syndrome (MetS) among hemodialysis patients and factors associated with quality of dialysis. METHODS: Data were collected from 377 long-term hemodialysis patients who received hemodialysis treatment from clinics in Tainan and Kaohsiung between November 2009 and February 2010. MetS was defined using the criteria set in 2007 by the Bureau of Health Promotion, Department of Health, Taiwan. The measurement of Kt/V was used as an indicator of the quality of dialysis. A below 1.4 Kt/V was considered poor dialysis quality. RESULTS: Results showed that the prevalence of MetS among the chronic hemodialysis patients in this sample was 63.1%. Logistic regression results identified that the quality of dialysis in females was better than that in males (odds ratio (OR)=7.98, 95% confidence interval (CI): 2.52-25.31). Better quality dialysis was associated with older age, longer treatment time, and increased blood flow rate (OR=1.49, 13.63, and 1.35, respectively). However, for every one kilogram increase in weight, the quality of dialysis decreased by 13 percents (OR=0.87, 95% CI: 0.83-0.92). CONCLUSIONS: MetS is common among hemodialysis patients. The prevalence of hypertension, hyperlipidemia, and hyperglycaemia were significantly higher among hemodialysis patients. Quality of dialysis related to gender, age, weight, and the dialysis prescription (treatment time and blood flow rate).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".