Cardiovascular and Noncardiovascular Mortality among Men and Women Starting Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although women have a survival advantage in the general population, women on dialysis have similar mortality to men. We hypothesized that this paired mortality risk during dialysis may be explained by a relative excess of cardiovascular-related mortality in women. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We compared 5-year age-stratified cardiovascular and noncardiovascular mortality rates, relative risks, and hazard ratios in a European cohort of incident adult dialysis patients (European Renal Association-European Dialysis and Transplant Association [ERA-EDTA] Registry) with the European general population (Eurostat). Cause of death was recorded by ERA-EDTA codes in dialysis patients and by International Statistical Classification of Diseases codes in the general population. RESULTS: Overall, sex did not have a predictive effect on outcome in dialysis. Stratification into age categories and causes of death showed greater noncardiovascular mortality in young women (<45 years). In other age categories (45 to 55 and >55 years), women presented lower cardiovascular mortality. This cardiovascular benefit was, however, smaller than in the general population. Stratification by diabetic nephropathy showed that diabetic women in all age categories remained at increased mortality risk compared with men, an effect mainly attributed to the noncardiovascular component. CONCLUSIONS: Mortality rates and causes of death in men and women on dialysis vary with age. Increased noncardiovascular mortality may explain the loss of the survival advantage of women on dialysis. Both young and diabetic women starting dialysis are at a higher mortality risk than equal men.
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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 | 0.001 |
| 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) | 0.002 | 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".