Prospective Determination of the Incidence and Risk Factors of New‐Onset Uveitis in Juvenile Idiopathic Arthritis: The Research in Arthritis in Canadian Children Emphasizing Outcomes Cohort
Bibliographic record
Abstract
OBJECTIVE: Identification of the incidence of juvenile idiopathic arthritis (JIA)-associated uveitis and its risk factors is essential to optimize early detection. Data from the Research in Arthritis in Canadian Children Emphasizing Outcomes inception cohort were used to estimate the annual incidence of new-onset uveitis following JIA diagnosis and to identify associated risk factors. METHODS: Data were reported every 6 months for 2 years, then yearly to 5 years. Incidence was determined by Kaplan-Meier estimators with time of JIA diagnosis as the reference point. Univariate log-rank analysis identified risk factors and Cox regression determined independent predictors. RESULTS: In total, 1,183 patients who enrolled within 6 months of JIA diagnosis met inclusion criteria, median age at diagnosis of 9.0 years (interquartile range [IQR] 3.8-12.9), median follow-up of 35.2 months (IQR 22.7-48.3). Of these patients, 87 developed uveitis after enrollment. The incidence of new-onset uveitis was 2.8% per year (95% confidence interval [95% CI] 2.0-3.5) in the first 5 years. The annual incidence decreased during follow-up but remained at 2.1% (95% CI 0-4.5) in the fifth year, although confidence intervals overlapped. Uveitis was associated with young age (<7 years) at JIA diagnosis (hazard ratio [HR] 8.29, P < 0.001), positive antinuclear antibody (ANA) test (HR 3.20, P < 0.001), oligoarthritis (HR 2.45, P = 0.002), polyarthritis rheumatoid factor negative (HR 1.65, P = 0.002), and female sex (HR 1.80, P = 0.02). In multivariable analysis, only young age at JIA diagnosis and ANA positivity were independent predictors of uveitis. CONCLUSION: Vigilant uveitis screening should continue for at least 5 years after JIA diagnosis, and priority for screening should be placed on young age (<7 years) at JIA diagnosis and a positive ANA test.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".