The excess burden of osteoarthritis in the province of Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Little is known about the burden of osteoarthritis (OA) in Canada. This study was undertaken to estimate the excess burden of OA in Ontario, the largest province in Canada. METHODS: The records of Ontarian respondents to the Canadian Community Health Survey (CCHS) who provided consent to data linkage were linked to the Ontario Health Insurance Program physician claims database and the Discharge Abstract Database Inpatient and Day Procedure databases. Patients with OA (n = 1,474) were identified using CCHS 1.1 and the physician claims database. To determine the excess burden of OA, a control group matched by age, sex, and rural/urban status was created, with 3 controls per case (n = 4,422). Sociodemographic and medical characteristics, health-related quality of life, and 1-year physician, day (outpatient) procedure, and hospitalization costs were compared between the 2 groups. Regression analyses were performed to identify predictors of medical characteristics, health utility, and cost. RESULTS: The mean age of the OA patients and the control subjects was 66 years, and 74% of all study subjects were women. Several differences were observed between patients with OA and subjects without OA in terms of socioeconomic and medical characteristics. On a scale of 0-1, the mean utility value associated with OA was 0.68, compared to 0.84 for the control group (P < 0.0001), representing a utility decrement of 0.16. The 1-year physician, outpatient procedure, and hospitalization costs were significantly higher in the OA group than in the non-OA group ($2,233 Canadian versus $1,033 Canadian, respectively; P < 0.0001). CONCLUSION: These results indicate that the excess burden of OA in Ontario is considerable.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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".