Risk factors for development of uveitis differ between girls and boys with juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVE: Uveitis is the most common extraarticular manifestation of juvenile idiopathic arthritis (JIA) and is associated with considerable morbidity. The aim of this study was to examine the risk factors associated with uveitis in JIA. METHODS: We conducted a chart review of 1,047 patients with JIA from a single tertiary care pediatric rheumatology center for factors associated with the development of uveitis. Special emphasis was put on the following known risk factors: oligoarthritis, antinuclear antibody (ANA) status, sex, and age at the time of onset of JIA. RESULTS: The risk of uveitis developing was age dependent in girls but not in boys. Among girls, the risk was maximal (47%) in those who were ANA positive and were ages 1-2 years at the time of the onset of JIA; this risk decreased to <10% in those in whom the age at onset was >7 years. Only girls had an age-dependent and ANA-associated increased risk of uveitis. The time interval from the diagnosis of JIA to the diagnosis of uveitis was statistically significantly longer in patients in whom the onset of JIA occurred at a younger age (P = 0.04). This effect was even more pronounced in ANA-positive patients (P = 0.004). The JIA subtype did not influence a patient's risk of the development of uveitis. CONCLUSION: An age-associated risk of uveitis was observed only in girls who were younger than 7 years of age at the time of the onset of JIA. The duration of time between the diagnosis of JIA and the onset of uveitis was longer in patients in whom JIA was diagnosed at a younger age, especially in those who were ANA positive. We suggest that our findings have implications for uveitis screening in patients with JIA.
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".