Impact of remote location on quality care delivery and relationships to adverse health outcomes in patients with diabetes and chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: To investigate the relation of residence location, markers of good quality healthcare and adverse clinical outcomes in patients with diabetes and chronic kidney disease (CKD). METHODS: We identified 31 337 individuals with diabetes and estimated glomerular filtration rate (eGFR) 15-59 mL/min/1.73 m(2) from a population-based cohort (n= 1 278 375) of adults with serum creatinine measured at least once during 2005 or 2006 in Alberta, Canada. The study population was classified into categories based on travel distance by road from residence location to the closest nephrologist: (0-50, 50.1-100, 100.1-200 and >200 km). RESULTS: At follow-up, compared with those living within 50 km, remote dwellers were less likely to visit a nephrologist, less likely to have hemoglobin A1c and urinary albumin measured within 1 year of the index eGFR, and less likely to receive an angiotensin converting enzyme inhibitor, angiotensin receptor blocker or statin (all P < 0.0001). In adjusted models, compared with those with CKD (Stage 3 or 4) living within 50 km, the adjusted likelihood of all-cause hospitalization was [1.4 (95% confidence interval, CI, 1.3-1.6)], [1.3 (95% CI, 1.1-1.6)] and [1.3 (95% CI, 1.2-1.5)]-fold higher for patients living 50.1-100, 100.1-200 and >200 km away from a nephrologist, respectively (P < 0.0001). The hazard ratio of all-cause mortality increased with increasing distance: [1.07 (95% CI, 0.9-1.2)], [1.1 (95% CI, 0.9-1.2)] and [1.2 (95% CI, 1.0-1.4)], respectively (P < 0.0001). CONCLUSIONS: Compared with those living closer to a nephrologist, remote dwellers with diabetes and CKD were less likely to receive recommended quality care, and more likely to experience adverse health outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".