Trends in physician‐diagnosed osteoarthritis incidence in an administrative database in British Columbia, Canada, 1996–1997 through 2003–2004
Bibliographic record
Abstract
OBJECTIVE: Prevalence of osteoarthritis (OA) is expected to increase due to population aging. However, there is little information on the trends in the incidence of OA over time. The purpose of this study was to describe changes in physician-diagnosed OA incidence rates between 1996-1997 and 2003-2004 in British Columbia (BC), Canada. METHODS: We used data on all visits to health professionals and hospital admissions covered by the Medical Services Plan of BC (population approximately 4 million) for the fiscal years 1991-1992 through 2003-2004. Rates were standardized to the BC population in 2000. We used 2 definitions of OA: 1) at least 1 visit or hospitalization with a diagnostic code for OA, and 2) at least 2 visits or 1 hospitalization with a code for OA. Incidence rates were calculated with a 5-year run-in period to exclude prevalent cases. RESULTS: Between 1996-1997 and 2003-2004, crude incidence rates of OA based on definition 1 increased from 10.5 to 12.2 per 1,000 in men and from 13.9 to 17.4 per 1,000 in women. The age-standardized rates did not change in men and increased from 14.7 to 16.7 per 1,000 in women. Incidence rates based on definition 2 were almost 50% lower, but the trends were similar. CONCLUSION: We observed an increase in the incidence of OA in both men and women due to population aging and an additional increase in women beyond the effect of aging. These trends have important implications for public health and provision of health services to this very large group of patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".