Capturing the Range of Disease Involvement in Localized Scleroderma: The Localized Scleroderma Total Severity Scale
Bibliographic record
Abstract
OBJECTIVE: Juvenile localized scleroderma (jLS) is a chronic autoimmune disease commonly associated with poor outcomes, including contractures, hemiatrophy, uveitis, and seizures. Despite improvements in treatment, >25% of patients with jLS have functional impairment. To improve patient evaluation, our workgroup developed the Localized scleroderma Total Severity Scale (LoTSS), an overall disease severity measure. METHODS: LoTSS was developed as a weighted measure by a consensus process involving literature review, surveys, case vignettes, and multicriteria decision analysis. Feasibility was assessed in larger Childhood Arthritis and Rheumatology Research Alliance groups. Construct validity with physician assessment and inter-rater reliability was assessed using case vignettes. Additional evaluation was performed in a prospective patient cohort initiating treatment. RESULTS: LoTSS severity items were organized into modules that reflect jLS disease patterns, with modules for skin, extracutaneous, and craniofacial manifestations. Construct validity of LoTSS was supported by a strong positive correlation with the Physician Global Assessment (PGA) of severity and damage and weak positive correlation with PGA-Activity, as expected. LoTSS was responsive, with a small effect size identified. Moderate-to-excellent inter-rater reliability was demonstrated. LoTSS was able to discriminate between patient subsets, with higher scores identified in those with greater disease burden and functional limitation. CONCLUSION: We developed a new LS measure for assessing cutaneous and extracutaneous severity and have shown it to be reliable, valid, and responsive. LoTSS is the first measure that assesses and scores all the major extracutaneous manifestations in LS. Our findings suggest LoTSS could aid assessment and management of patients and facilitate outcome evaluation in treatment 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".