Clinical Characteristics of Children With Juvenile Dermatomyositis: The Childhood Arthritis and Rheumatology Research Alliance Registry
Bibliographic record
Abstract
OBJECTIVE: To investigate aspects of juvenile dermatomyositis (DM), including disease characteristics and treatment, through a national multicenter registry. METHODS: Subjects meeting the modified Bohan and Peter criteria for definite juvenile DM were analyzed from the cross-sectional Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry between 2010 and 2012 from 55 US pediatric rheumatology centers. Demographics, disease characteristics, diagnostic assessments, and medication exposure data were collected at enrollment. RESULTS: A total of 384 subjects met the criteria for analysis. At enrollment, the median Childhood Myositis Assessment Scale score was 51 (interquartile range [IQR] 46-52), the median Childhood Health Assessment Questionnaire score was 0 (IQR 0-0.5), and the median physician and subject global assessment scores were 1 (IQR 0-2) and 1 (IQR 0-3), respectively, out of a maximum of 10. Of the diagnostic assessments, magnetic resonance imaging was more likely than electromyography or muscle biopsy to show abnormalities. A total of 329 subjects had ≥2 diagnostic studies performed, and >34% of these subjects reported ≥1 negative study. Ninety-five percent had been treated with corticosteroids and 92% with methotrexate, suggesting that these medications were almost universally prescribed for juvenile DM in the US. CONCLUSION: In 2 years, the ongoing CARRA Registry has collected clinical data on 384 children with juvenile DM and has the potential to become one of the largest juvenile DM cohorts in the world. More research is needed about prognostic factors in juvenile DM, and differences in therapy based on manifestations of disease need to be explored by practitioners. This registry provides the infrastructure needed to advance clinical and translational research and represents a major step toward improving outcomes of children with juvenile DM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".