Incidence and prevalence of giant cell arteritis in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To estimate trends in the incidence and prevalence of GCA over time in Canada. METHODS: We performed a population-based study of Ontario health administrative data using validated case definitions for GCA. Among Ontario residents ≥50 years of age we estimated the annual incidence and prevalence rates between 2000 and 2018. We performed sensitivity analyses using alternative validated case definitions to provide comparative estimates. RESULTS: Between 2000 and 2018 there was a relatively stable incidence over time with 25 new cases per 100 000 people >50 years of age. Age-standardized incidence rates were significantly higher among females than males [31 cases (95% CI: 29, 34) vs 15 cases (95% CI: 13, 18) per 100 000 in 2000]. Trends in age-standardized incidence rates were stable among females but increased among males over time. Incidence rates were highest among those ≥70 years of age. Standardized prevalence rates increased from 125 (95% CI 121, 129) to 235 (95% CI 231, 239) cases per 100 000 from 2000 to 2018. The age-standardized rates among males rose from 76 (95% CI 72, 81) cases in 2000 to 156 (95% CI 151, 161) cases per 100 000 population in 2018. Between 2000 and 2018, the age-standardized rates among females similarly increased over time, from 167 (95% CI 161, 173) to 304 (95% CI 297, 310) cases per 100 000 population. CONCLUSION: The incidence and prevalence of GCA in Ontario is similar to that reported in the USA and northern Europe and considerably higher than that reported for southern Europe and non-European populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".