Association between proximity to the attending nephrologist and mortality among patients receiving hemodialysis
Bibliographic record
Abstract
BACKGROUND: Many Canadian patients who receive hemodialysis live far from their attending nephrologist, which may affect clinical outcomes. We investigated whether patients receiving hemodialysis who live farther from their attending nephrologist are more likely to die than those who live closer. METHODS: We studied a random sample of 18,722 patients who began hemodialysis between 1990 and 2000 in Canada. We calculated the distance between each patient's residence location at the start of dialysis and the practice location of their attending nephrologist. We used Cox proportional hazards models to examine the adjusted relation between distance and clinical outcomes (death from all causes, infectious causes and cardiovascular causes) over a follow-up period of up to 14 years. RESULTS: During the follow-up period (median 2.5 yr, interquartile range 1.0-4.7 yr), 11,582 (62%) patients died. Compared with patients who lived within 50 km of their nephrologist, the adjusted hazard ratio of death among those who lived 50.1-150 km away was 1.06 (95% confidence interval [CI] 1.01-1.12), 1.13 (95% CI 1.04-1.22) for those who lived 150.1-300 km away and 1.13 (95% CI 1.03-1.24) for those who lived more than 300 km from their nephrologist (p for trend < 0.001). The risk of death from infectious causes increased with greater distance from the attending nephrologist (p for trend < 0.001). The risk of death from cardiovascular causes did not increase with distance from the attending nephrologist (p for trend = 0.21). Compared with patients who lived within 50 km of their nephrologist, the adjusted hazard ratio of death among those who lived more than 300 km away was 1.75 (95% CI 1.32-2.32) for infectious causes and 0.93 (95% CI 0.79-1.09) for cardiovascular causes. CONCLUSIONS: Mortality associated with hemodialysis was greater among patients who lived farther from their attending nephrologist, as compared with those who lived closer. This was especially evident for death from infectious causes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".