The Epidemiology of Rheumatoid Arthritis in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Epidemiologic assessments of sufficiently large populations are required in order to obtain robust estimates of disease prevalence and incidence, particularly when exploring the influence of various factors (age, sex, calendar time). We undertook this study to describe the epidemiology of rheumatoid arthritis (RA) over the past 15 years. METHODS: We used the Ontario Rheumatoid Arthritis administrative Database (ORAD), a validated population-based research database of all Ontarians with RA. The ORAD records were linked with census data to calculate crude and age and sex-standardized prevalence and incidence rates from 1996 to 2010. Vital statistics were used to estimate annual all-cause mortality during the study period. RESULTS: As of 2010, there were 97,499 Ontarians with RA, corresponding to a cumulative prevalence of 0.9%. Age and sex-standardized RA prevalence increased steadily over time from 473 (95% confidence interval [95% CI] 469-478) per 100,000 population (0.49%) in 1996 to 784 (95% CI 779-789) per 100,000 population (0.9%) in 2010. Age and sex-standardized incidence per 100,000 population ranged from 62 (95% CI 60-63) in 1996 to 54 (95% CI 52-55) in 2010. All-cause mortality decreased by a relative 21.4% since 1996. CONCLUSION: Over a 15-year period, we observed an increase in RA prevalence over time. This rise may be attributed to the increasing time to ascertain cases (which may have been latent in the population during earlier years of the study), increasing survival, and/or an increase in the aging background population. Incidence appears to be stable.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.003 | 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".