Osteoarthritis Incidence and Trends in Administrative Health Records from British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To calculate the incidence rates of osteoarthritis (OA) and to describe the changes in incidence using 18 years of administrative health records. METHODS: We analyzed visits to health professionals and hospital admission records in a random sample (n = 640,000) from British Columbia, Canada, from 1991/1992 through 2008/2009. OA was defined in 2 ways: (1) at least 1 physician diagnosis or 1 hospital admission; and (2) at least 2 physician diagnoses in 2 years or 1 hospital admission. Crude and age-standardized rates were calculated, and the annual relative changes were estimated from the Poisson regression models. RESULTS: In 2008/2009, the overall crude incidence rate (95% CI) of OA using definition 1 was 14.6 (14.0-14.8); [12.5 (12.0-13.0) among men and 16.3 (15.8-16.8) among women] per 1000 person-years. The rates were lower by about 44% under definition 2. For the period 2000/2001-2008/2009, crude incidence rates based on definition 1 varied from 11.8 to 14.2 per 1000 person-years for men, and from 15.7 to 18.5 for women. Annually, on average, crude rates rose by about 2.5-3.3% for both men and women. The age-adjusted rates increased by 0.6-0.8% among men and showed no trend among women. CONCLUSION: Our study generated updated incidence rates of administrative OA for the Province of British Columbia. Physician-diagnosed overall incidence rates of OA varied with the case definitions used; however, trends were similar in both case definitions. Age-adjusted rates among men increased slightly during the period 2000/2001-2008/2009. These findings have implications for projecting future prevalence and costs of OA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".