Exploratory Spatial Analysis of Osteoarthritis Patients in Alberta
Bibliographic record
Abstract
Equitable access to Osteoarthritis (OA) health services in Alberta is challenged by the geographic spread of the Alberta population coupled with variations in OA prevalence across the province. OA is a degenerative chronic condition affecting 10-15% of adults in Canada. Our goal was to determine geographic variations of patients with OA, considering their needs for access to specialty and non-specialty OA-related health services use in Alberta. To reveal the geographic variations, we used longitudinal administrative health records from which we identified 323,674 OA prevalence cohort cases in Alberta (April 1, 2012 –March 31, 2013). Our analysis showed significantly larger numbers of prevalence cases for women than men (p-value<0.001). There were significantly higher age- and sex-standardized OA prevalence rates per 1,000 population in rural remote areas, rural areas, satellite communities located on the periphery of the city of Edmonton (moderate metro areas), and in moderate urban areas in the centre. Our hot spot analysis results showed local hot spots in Alberta that were particularly consistent with already identified communities with high numbers of elderly patients, and patients with comorbidities and/or low socio-economic status. The specialty care weighted hot spot analysis showed slightly higher numbers of hot spots in communities from rural remote and rural south areas, compared to other areas where patients mostly used non-specialty health services. This information will help inform the distribution and delivery of healthcare resources to communities with high OA prevalence in Alberta
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 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".