Occurrence and Risk Factors of Uveitis in Juvenile Psoriatic Arthritis: Data From a Population-based Nationwide Study in Germany
Bibliographic record
Abstract
Objective. Data on uveitis in juvenile psoriatic arthritis (JPsA), a category of juvenile idiopathic arthritis (JIA), are scarce. We describe prevalence and risk factors for JPsA-associated uveitis (JPsA-U). Methods. Cross-sectional data from the German National Pediatric Rheumatological Database (2002–2014) were used to characterize JPsA-U and assess risk factors for the development of uveitis. Results. Uveitis developed in 6.6% of 1862 patients with JPsA. Patients with JPsA-U were more frequently female (73.0 vs 62.9%, P = 0.03), antinuclear antibody (ANA) positive (60.3 vs 37.0%, P < 0.001), younger at JPsA onset (5.3 ± 4.1 vs 9.3 ± 4.4 yrs, P < 0.001), and treated with disease-modifying antirheumatic drugs (DMARDs) significantly more frequently compared with JPsA patients without uveitis. On a multivariable analysis of a subgroup of 655 patients enrolled in the study ≤ 1 year after arthritis onset, mean clinical Juvenile Arthritis Disease Activity Score for 10 joints during study documentation was significantly associated with uveitis development. Children with early onset of JPsA (aged < 5 yrs vs ≥ 5 yrs) were significantly more frequently ANA positive (48.4% vs 35.7%, P < 0.001), affected by uveitis (17.3% vs 3.8%, P < 0.001), and treated with DMARDs (52.9% vs 43.8%, P < 0.001), but less often affected by skin disease (55.3% vs 61.0%, P = 0.03). Conclusion. The characteristics of patients with JPsA developing uveitis are similar to those of patients with uveitis in other JIA categories, such as oligoarticular JIA. Children with early-onset JPsA are at a higher risk for ocular involvement. Our data support the notion of a major clinical difference between those patients with early vs late onset of JPsA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".