Epidemiology of juvenile idiopathic arthritis in a multiethnic cohort: Ethnicity as a risk factor
Bibliographic record
Abstract
OBJECTIVE: To study the influence of ethnicity on the risk of developing juvenile idiopathic arthritis (JIA) in a multiethnic community of patients with unrestricted access to health care. METHODS: A questionnaire on ethnicity was distributed to all patients with JIA being followed up at the Hospital for Sick Children in Toronto, Ontario, Canada. Of 1,082 patients, 859 (79.4%) responded to the questionnaire. To calculate the relative risk (RR) of developing JIA in this study cohort, the results were compared with data from the age-matched general population of the Toronto metropolitan area (TMA) as provided in the 2001 census from Statistics Canada. RESULTS: European descent was reported by 69.7% of the patients with JIA compared with a frequency of 54.7% in the TMA general population, whereas a statistically significantly lower than expected percentage of the patients with JIA reported having black, Asian, or Indian subcontinent origin. Children of European origin had a higher RR for developing any of the JIA subtypes except polyarticular rheumatoid factor (RF)-positive JIA, and were particularly more likely to develop the extended oligoarticular and psoriatic subtypes. A higher frequency of enthesitis-related JIA was observed among patients of Asian origin, while those of black origin or native North American origin were more likely to develop polyarticular RF-positive JIA. CONCLUSION: In this multiethnic cohort, European descent was associated with a significantly increased risk of developing JIA, and the distribution of JIA subtypes differed significantly across ethnic groups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".