Juvenile Psoriatic Arthritis Inception Cohort in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry: Characteristics and Early Disease Outcomes
Bibliographic record
Abstract
OBJECTIVE: To characterize the demographics, disease characteristics, and treatment patterns of an inception cohort of children with juvenile psoriatic arthritis (JPsA) within the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry. METHODS: Patients diagnosed with JPsA within 6 months of CARRA registry enrollment were included and observed for up to 24 months. Baseline disease characteristics, treatment history, disease activity measures, and patient-reported outcomes (PROs) were captured at 6-month intervals (± 3 months) at usual care visits during the 24-month period. RESULTS: A total of 306 patients were included. Patients were predominantly female (62.4%), with a median age of onset of 11.0 (IQR 6.0-14.0) years. At CARRA registry enrollment, 52.3% had polyarticular-course JPsA, median active joint count was 3.0 (IQR 1.0-6.0), 20.1% had enthesitis, 34.3% had dactylitis, 9.5% had active sacroiliitis, and 58.8% had psoriasis. Tumor necrosis factor inhibitors were used in 61.1% of patients. In total, 20.5% of patients received treatment with ≥ 2 bDMARDs or traditional synthetic DMARDs. Clinical Juvenile Arthritis Disease Activity Score in 10 joints (cJADAS-10) improved from a median of 10.0 (IQR 5.5-15.0) at baseline to 1.0 (IQR 0.0-5.0) at 24 months. Improvements were also seen in active enthesitis and active sacroiliitis. CONCLUSION: In this inception cohort of JPsA in the CARRA registry, half of the patients had polyarticular presentation, and the majority required advanced therapy. Regardless of the treatment used, most patients had improvements in disease activity measures and PROs, with most achieving clinically inactive disease. However, escalation of treatment was common, highlighting the unmet need for precision medicine in identifying the optimal initial drug for each individual patient.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".