Physician practices for withdrawal of medications in inactive systemic juvenile arthritis, Childhood Arthritis and Rheumatology Research Alliance (CARRA) survey
Bibliographic record
Abstract
BACKGROUND: We describe a Childhood Arthritis and Rheumatology Research Alliance (CARRA) survey of North American pediatric rheumatologists that assesses physician attitudes on withdrawal of medications in systemic juvenile idiopathic arthritis (SJIA). METHODS: A REDCap anonymous electronic survey was distributed to 100 random CARRA JIA workgroup physician-voting members. The survey had three broad sections including: A) demographic information; B) physicians' opinions on clinical inactive disease (CID) in SJIA and C) existing practices for withdrawing medications in SJIA. RESULTS: The survey had an 86% response rate. 88 and 93% of participants agreed with the current criteria for CID and clinical remission on medications (CRM) respectively. 78% thought it necessary to meet CRM before tapering medications except steroids. 76% use CARRA SJIA consensus treatment plans always or the majority of the time. All participants weaned steroids first in SJIA patients on combination therapy, 47% waited > 6 months before tapering additional medications. 35% each tapered methotrexate over > 6 months and 2-6 months; however, 39% preferred tapering anakinra, canakinumab and tocilizumab more quickly over 2-6 months and favored spacing the dosing interval for canakinumab and tocilizumab. When patients are on combination therapy with methotrexate and biologics, 58% preferred tapering methotrexate first while others considered patient/family preference and adverse effects to guide their choice. CONCLUSION: Most CARRA members surveyed use published consensus treatment plans for SJIA and agree with validated definitions of CID and CRM. There was agreement with tapering steroids first in SJIA. There was considerable variability with tapering decisions of all other medications.
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 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".