Approach to Janus kinase inhibition for juvenile dermatomyositis among CARRA and PReS providers
Bibliographic record
Abstract
OBJECTIVES: Janus kinase inhibition (JAKi) has been proposed as a treatment for idiopathic inflammatory myopathies to target increased interferon signalling. Predominantly retrospective reports have demonstrated effectiveness of JAKi in refractory JDM. However, JAKi remains an off-label treatment for JDM and there may be variation in use worldwide. An international survey was conducted to investigate approaches to JAKi for JDM. METHODS: The Childhood Arthritis and Rheumatology Research Alliance (CARRA) JDM Therapeutics workgroup and core members of the Paediatric Rheumatology European Society (PReS) JDM working party devised an electronic survey to assess the use of JAKi in JDM. CARRA and PReS members were invited by e-mail to complete the survey. RESULTS: There were 229 respondents (18%), with 50% from the USA and 29% from Europe. One hundred and fifty had used JAKi for over 450 patients with JDM; among them, 77% noted clinical improvement in most or all patients and 17% reported side effects. The highest ranked perceived barriers to JAKi use were lack of clinical data and inability to obtain insurance approval. The highest ranked clinical indications for starting JAKi were refractory skin disease, refractory muscle disease, inability to wean steroids and intolerance to other steroid-sparing agents. CONCLUSION: Paediatric rheumatologists use JAKi off-label for refractory JDM. Most providers noted clinical improvement in their patients. Barriers to JAKi use include lack of clinical data and insurance coverage. Clinical trials are needed to provide better data on the efficacy and safety of JAKi.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".