Imbalance of Prevalence and Specialty Care for Osteoarthritis for First Nations People in Canada
Bibliographic record
Abstract
Objective: Estimate the population-based prevalence and healthcare use for osteoarthritis (OA) by First Nations (FN) and non-First Nations (non-FN) in Alberta. Methods: A cohort of adults with OA (≥2 physician claims in 2 years or 1 hospitalization with ICD-9-CM code 715x or ICD-10-CA code M15-19, years 1993-2010) was defined, with FN determination by premium payer status. Prevalence rates (2007/8) were estimated from the cohort and the population registered with the Alberta Health Care Insurance Plan. Rates of outpatient primary care and specialist (orthopedics, rheumatology, internal medicine) visits; arthroplasty (hip and knee); and all-cause hospitalization were estimated. Results: OA prevalence in FN was twice that of the non-FN population (16.1 vs 7.8 cases/100 population; standardized rate ratio (SRR) adjusted for age and sex 2.06, 95%CI 2.00-2.12). The SRR (adjusted for age, sex and location of residence) for primary care visits for OA was nearly double in FN compared to non-FN (SRR 1.88, 95%CI 1.87-1.89), and internal medicine visits were increased (SRR 1.25, 95%CI 1.25-1.26). Visit rates with an orthopedic surgeon (SRR 0.49, 95%CI 0.48-0.50) or rheumatologist (SRR 0.62, 95%CI 0.62-0.63) were substantially lower in FN with OA. Hip and knee arthroplasties were performed less frequently in FN with OA (SRR 0.48, 95%CI 0.47-0.49), but all-cause hospitalization rates were higher (SRR 1.59, 95%CI 1.58-1.60). Conclusion: We estimate a 2-fold higher prevalence of OA in the FN population, with differential healthcare use. Reasons for higher use of primary care and lower use of specialty services and arthroplasty compared to the general population are not yet understood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".