Assessment, classification and treatment of calcinosis as a complication of juvenile dermatomyositis: a survey of pediatric rheumatologists by the childhood arthritis and rheumatology research alliance (CARRA)
Bibliographic record
Abstract
BACKGROUND: There is no standardized approach to the management of JDM-associated calcinosis and its phenotypes. Current knowledge of treatment outcomes is confined to small series and case reports. We describe physician perspectives toward diagnostic approach, classification and treatment directly targeting calcinosis, independent of overall JDM therapy. METHODS: An electronic survey of 22 questions was organized into sections regarding individual practices of assessment, classification and treatment of calcinosis, including perceived successes of therapies. Invitations to complete the survey voluntarily and anonymously were sent to CARRA physician members and the Pediatric Rheumatology Bulletin Board, an electronic list-serv. Results were analyzed by descriptive statistics and chi-square analyses. RESULTS: Of 139 survey responses, 118 were included in analysis. Of these, 70% were based in the USA and 88 (75%) were CARRA members. Only 17% of responders have seen more than 20 cases of calcinosis, and only 28% perform screening imaging studies on new JDM diagnoses. Increasing systemic immunosuppression is first-line therapy for 67% of respondents. Targeted therapy against calcinosis is most often instituted for symptomatic patients. IVIG and bisphosphonates are most frequently used and considered most successful, but many other agents are used. Experienced physicians are more likely to use bisphosphonates, calcium channel blockers and topical sodium thiosulfate (p< 0.002 or lower). CONCLUSIONS: Coexisting JDM disease activity influences whether calcinosis is considered active disease or targeted directly. Experience treating JDM-related calcinosis is low, as are rates of formal screening for calcinosis. Experienced physicians are more likely to use non-immunosuppressive treatments.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".