Disease Recapture Rates After Medication Discontinuation and Flare in Juvenile Idiopathic Arthritis: An Observational Study Within the Childhood Arthritis and Rheumatology Research Alliance Registry
Bibliographic record
Abstract
OBJECTIVE: Children with well-controlled juvenile idiopathic arthritis (JIA) frequently experience flares after medication discontinuation, but the outcomes of these flares have not been well described. The objective of this study was to characterize the rates and predictors of disease recapture among children with JIA who restarted medication to treat disease flare. METHODS: Children with JIA who discontinued conventional synthetic or biologic disease-modifying antirheumatic drugs for well-controlled disease but subsequently experienced a flare and restarted medication were identified from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) registry. The primary outcome was inactive disease (ID) (physician global assessment <1 and active joint count = 0) 6 months after flare. RESULTS: A total of 333 patients had complete data for ID at 6 months after flare. The recapture rate for the cohort was 55%, ranging from 47% (persistent oligoarthritis) to 69% (systemic arthritis) (P = 0.4). Approximately 67% of children achieved ID by 12 months. In the multivariable model, history and reinitiation of biologic drugs were associated with increased odds of successful recapture (odds ratio [OR] 4.79 [95% confidence interval (95% CI) 1.22-18.78] and OR 2.74 [95% CI 1.62-4.63], respectively). Number of joints with limited range of motion was associated with decreased odds (OR 0.83 per 1 joint increase [95% CI 0.72-0.95]). CONCLUSION: Approximately half of JIA flares post-discontinuation were recaptured within 6 months, but rates of recapture varied across JIA categories. These findings inform shared decision-making for patients, families, and clinicians regarding the risks and benefits of medication discontinuation. Better understanding of biologic predictors of successful recapture in JIA are needed.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".