Associations of marital status with mortality from all causes and mortality from cardiovascular disease in Japanese haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Marital status is an important social factor associated with increased mortality from cardiovascular disease (CVD) and all causes. However, there has been no study on the association of marital status with mortality in haemodialysis patients. METHODS: We analysed data from a 5-year prospective cohort study of 1064 Japanese haemodialysis patients aged 30 years or older. Marital status was classified into three groups: married, single and divorced/widowed. Cox's regression was used to estimate multivariate hazard ratios (HRs) [95% confidence intervals (CIs)] for all-cause mortality and CVD mortality according to marital status after adjusting for age, sex, duration of haemodialysis, cause of renal failure, body mass index, systolic blood pressure, total cholesterol, high density lipoprotein-cholesterol, albumin, high-sensitivity C-reactive protein, co-morbid conditions, smoking, alcohol consumption, education levels and job status. RESULTS: Single patients had higher risks than married patients for mortality from all causes (HR = 1.51, 95% CI: 1.06-2.16) and mortality from CVD (HR = 1.68, 95% CI: 1.03-2.76), and divorced/widowed patients had a higher risk than married patients for mortality from CVD (HR = 1.73, 95% CI: 1.15-2.60). After stratification by age, single patients aged 30-59 years had significantly higher risks for all-cause mortality and CVD mortality. CONCLUSIONS: The findings suggest that single status is a significant predictor for all-cause mortality and CVD mortality and that divorced/widowed status is a significant predictor for CVD mortality in haemodialysis patients.
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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) | 0.000 | 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".