Childhood Arthritis and Rheumatology Research Alliance Biologic Disease‐Modifying Antirheumatic Drug Consensus Treatment Plans for Refractory Moderately Severe Juvenile Dermatomyositis
Bibliographic record
Abstract
OBJECTIVE: The objective was to develop consensus treatment plans (CTPs) for patients with refractory moderately severe juvenile dermatomyositis (JDM) treated with biologic disease-modifying antirheumatic drugs (bDMARDs). METHODS: The Biologics Workgroup of the Childhood Arthritis and Rheumatology Research Alliance JDM Research Committee used case-based surveys, consensus framework, and nominal group technique to produce bDMARD CTPs for patients with refractory moderately severe JDM. RESULTS: Four bDMARD CTPs were proposed: tumor necrosis factor α (TNFα) inhibitor (adalimumab or infliximab), abatacept, rituximab, and tocilizumab. Each CTP has different options for dosing and/or route. Among 76 respondents, consensus was achieved for the proposed CTPs (93% [67 of 72]) as well as for patient characteristics, assessments, outcome measures, and follow-up. By weighted average, respondents indicated that they would most likely administer rituximab, followed by abatacept, TNFα inhibitor, and tocilizumab. CONCLUSION: CTPs for the administration of bDMARDs in refractory moderately severe JDM were developed using consensus methodology. The implementation of the bDMARD CTPs will lay the groundwork for registry-based prospective comparative effectiveness studies.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".