Mortality Risk for Women on Chronic Hemodialysis Differs by Age
Bibliographic record
Abstract
BACKGROUND: Previous reports have demonstrated similar survival for men and women on hemodialysis, despite women's increased survival in the general population. OBJECTIVES: To examine the effect of age on mortality in women undergoing chronic hemodialysis. DESIGN: A retrospective cohort study using an administrative data registry, the Canadian Organ Replacement Registry (CORR) from Jan. 2001 and Dec. 2009. SETTING: Canada. PATIENTS: 28,971 (Women 11,792 (40.7%), Men 17,179 (59.3%)) incident chronic hemodialysis patients who survived greater than 90 days on dialysis. MEASUREMENTS: All-cause mortality. METHODS: Cox proportional hazards and competing risks models were employed to determine the independent association between sex, age and likelihood of all-cause mortality with renal transplantation as the competing outcome. RESULTS: During the study period, 6060 (51.4%) of women and 8650 (50.4%) of men initiating dialysis died. Younger women experienced higher mortality (Age < 45: Women 22.5%, Men 18.2%, hazard ratio (HR) 1.31 (1.12-1.52)) whereas elderly women experience lower mortality (Age 75-85: Women 65%, Men 67.3%, HR 0.94 95% CI 0.88-0.99, Age > 85: Women 66%, Men 70.2%, HR 0.83 95% CI 0.71-0.97) compared to men. This relationship persisted after accounting for the competing risk of transplantation. LIMITATIONS: The cause of death was unknown. CONCLUSIONS: Women's survival on chronic hemodialysis varies by age compared to men with a significantly higher mortality in women younger than 45 years old and lower mortality in woman older than 75 years of age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".