Treatment of Pediatric Localized Scleroderma: Results of a Survey of North American Pediatric Rheumatologists
Bibliographic record
Abstract
OBJECTIVE: We surveyed pediatric rheumatologists (PR) in North America to learn how they treat pediatric localized scleroderma (LS), a disease associated with significant morbidity for the growing child. METHODS: A Web-based survey was sent to the 195 PR members of the pediatric rheumatology research alliance CARRA (Childhood Arthritis and Rheumatology Research Alliance). Members were asked which medications they use to treat LS and which factors modify their treatment strategies. Clinical vignettes were provided to learn the specific treatment regimens used. RESULTS: A total of 158 PR from over 70 clinical centers in the United States and Canada participated in the survey, representing 81% of the CARRA membership. These PR saw over 650 patients with LS in the prior year. Nearly all respondents treated LS with methotrexate (MTX) and corticosteroids; most of them intensify treatment for lesions located on the face or near a joint, and about half intensify treatment for recent disease onset (< 6 months). Most PR reserve topical medications for limited treatment situations. Clinical vignettes showed that PR use a broad range of treatment doses and durations for MTX and corticosteroids. CONCLUSION: Most PR in North America treat localized scleroderma with a combination of MTX and corticosteroids. However, there is no consensus on specific treatment regimens. There is a need for controlled treatment trials to better determine optimal therapy for this potentially disabling disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".