Revisiting the association between altitude and mortality in dialysis patients
Bibliographic record
Abstract
It was recently reported that residential altitude is inversely associated with all-cause mortality among incident dialysis patients; however, no adjustment was made for key case-mix and laboratory variables. We re-examined this question in a contemporary patient database with comprehensive clinical and laboratory data. In a contemporary 8-year cohort of 144,892 maintenance dialysis patients from a large dialysis organization, we examined the relationship between residential altitude and all-cause mortality. Using data from the US Geological Survey, the average residential altitudes per approximately 43,000 US zip codes were compiled and linked to the residential zip codes of each patient. Mortality risks for these patients were estimated by Cox proportional hazard ratios. The study population's mean ± standard deviation age was 61 ± 15 years. Forty-five percent of patients were women, and 57% of patients had diabetes. In fully adjusted analysis, those residing in the highest altitude strata (≥ 6000 ft) had a lower all-cause mortality risk in fully adjusted analyses: death hazard ratio: 0.92 (95% confidence interval, 0.86-0.99), as compared with patients in the reference group (<250 ft). Residential altitude is inversely associated in all-cause mortality risk in maintenance dialysis patients notwithstanding the unknown and unmeasured confounders.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".