Prevalence, risk factors, and outcome of uveitis in juvenile idiopathic arthritis: A long‐term followup study
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence, risk factors, and long-term outcome of uveitis in patients with juvenile idiopathic arthritis (JIA). METHODS: An inception cohort of all 1,081 patients diagnosed as having JIA at a single tertiary care center was established. A questionnaire and followup telephone calls were used to confirm the diagnosis of uveitis. Ophthalmologists' records of patients with uveitis were collected. Kaplan-Meier and Cox regression analyses were used to assess risk factors for developing uveitis and for complications of uveitis. RESULTS: After a mean followup time of 6.9 years, 142 of 1,081 patients (13.1%) had developed uveitis. Risk factors were young age at diagnosis, female sex, antinuclear antibody positivity, and the subtype of JIA. The relative contribution of these risk factors was different for the different subtypes of JIA. Until the end of the study, uveitis complications had developed in 53 of 142 patients with uveitis (37.3%; 4.9% of the total cohort). Only 16 of 175 involved eyes (9.1%) in 14 of 108 patients (13%; 1.3% of the total cohort) for whom ophthalmology reports were available had best corrected visual acuity less than 20/40 (mean followup time for uveitis of 6.3 years). Abnormal vision was associated with synechiae or cataract. CONCLUSION: Risk factors for developing uveitis were different among subtypes of JIA. The long-term outcome of JIA-associated uveitis in our cohort was excellent despite the high rate of complications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".