New Features for Measuring Disease Activity in Pediatric Localized Scleroderma
Bibliographic record
Abstract
OBJECTIVE: To identify clinical features that define disease activity in pediatric localized scleroderma (LS), and determine their specificity and importance. METHODS: We conducted a multicenter prospective study of patients with active and inactive LS skin lesions. A standardized evaluation of a single designated study lesion per subject was performed at 3 visits. We evaluated the pattern and correlation between assessed features and physician's global assessments of activity (PGA-A). RESULTS: Ninety of 103 subjects had evaluable data; 66 had active and 24 inactive disease. Subjects had similar age of onset, sex, and disease patterns. Linear scleroderma was the most common subtype. Features specific for active disease included erythema, violaceous color, tactile warmth, abnormal skin texture, and disease extension. Scores for these variables changed over time and correlated with PGA-A of the lesion. Active and inactive lesions could not be distinguished by the presence or level of skin thickening, either of lesion edge or center. However, in active lesions, skin thickening scores did correlate with PGA-A scores. Regression analysis identified the combination of erythema, disease extension, violaceous color, skin thickening, and abnormal texture as predictive of PGA-A at study entry. Damage features were common irrespective of activity status. CONCLUSION: We identified variables strongly associated with disease activity, expanding upon those used in current measures, and determined their relative importance in physician activity scoring. Skin thickening was found to lack specificity for disease activity. These results will help guide development of a sensitive, responsive activity tool to improve care of patients with LS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".