Spatial analysis to locate new clinics for diabetic kidney patients in the underserved communities in Alberta
Bibliographic record
Abstract
BACKGROUND: Canadians with chronic diseases often live far away from healthcare facilities, which may compromise their level of care. We used a new method for selecting optimal locations for new healthcare facilities in remote regions. METHODS: We used a provincial laboratory database linked to data from the provincial health ministry. From all patients with serum creatinine measured at least once between 2002 and 2008 in Alberta, Canada, we selected those with diabetes and an estimated glomerular filtration rate (eGFR) of 15-60 mL/min/1.73 m(2). We then used two methods to select potential locations for new clinics that would serve the greatest number of remote-dwelling patients: plots showing the unadjusted density of such patients per 100 km(2) and SatScan analysis presenting the prevalent clusters of patients on the basis of chronic kidney disease (CKD) rates (adjusted for population size). RESULTS: We studied 32,278 patients with concomitant diabetes and CKD. A substantial number of patients (8%) resided >200 km from existing nephrologists' clinics. Density plots mapped with ArcGIS were useful for localizing a large cluster of underserved patients. However, objective assessment with SatScan technique and ArcGIS permitted us to detect additional clusters of patients in the northwest and southeast regions of Alberta--and suggested potential locations for new clinics in these areas. CONCLUSIONS: Objective techniques such as SatScan can identify clusters of underserved patients with CKD and identify potential new facility locations for consideration by decision-makers. Our findings may also be applicable to patients with other chronic diseases.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".