Attitudes and Approaches for Withdrawing Drugs for Children with Clinically Inactive Nonsystemic JIA: A Survey of the Childhood Arthritis and Rheumatology Research Alliance
Bibliographic record
Abstract
OBJECTIVE: To assess the attitudes and strategies of pediatric rheumatology clinicians toward withdrawing medications for children with clinically inactive juvenile idiopathic arthritis (JIA). METHODS: Members of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) completed an anonymous electronic survey on decision making and approaches for withdrawing medications for inactive nonsystemic JIA. Data were analyzed using descriptive statistics. RESULTS: Of 388 clinicians in CARRA, 124 completed surveys (32%), predominantly attending pediatric rheumatologists. The most highly ranked factors in decision making for withdrawing medications were the duration of clinical inactivity, drug toxicity, duration of prior activity, patient/family preferences, joint damage, and JIA category. Diagnoses of rheumatoid factor-positive polyarthritis and persistent oligoarthritis made respondents less likely and more likely, respectively, to withdraw JIA medications. Three-quarters of respondents waited for 6-12 months of inactive disease before stopping methotrexate (MTX) or biologics, but preferences varied. There was also considerable variability in the strategies used to reduce, taper, or stop medications for clinically inactive JIA; most commonly, clinicians reported slow medication tapers lasting at least 2 months. For children receiving combination MTX-biologic therapy, 63% of respondents preferred stopping MTX first. Most clinicians reported using imaging only seldom or sometimes to guide decision making, but most were also reluctant to withdraw medications in the presence of asymptomatic imaging abnormalities suggestive of subclinical inflammation. CONCLUSION: Considerable variability exists among pediatric rheumatology clinicians regarding when and how to withdraw medications for children with clinically inactive JIA. More research is needed to identify the most effective approaches to withdraw medications and predictors of outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".