Preliminary Validation of the Digital Ulcer Clinical Assessment Score in Systemic Sclerosis
Bibliographic record
Abstract
Objective. To date, “healed/non-healed” and clinical judgment are the only available assessment tools for digital ulcers (DU) in patients with systemic sclerosis (SSc). The aim of our study is to examine a preliminary composite DU clinical assessment score (DUCAS) for SSc for face, content, and construct validity. Methods. Patients with SSc presenting at least 1 finger DU were enrolled and assessed with the Health Assessment Questionnaire–Disability Index, Cochin scale, visual analog scale (VAS) for DU-related pain, patient global DU status, and global assessment as patient-reported outcomes (PRO), and physician VAS for DU status (phyGDU) as an SSc-DU expert physician/nurse measure. The DUCAS included 7 DU-related variables selected by a committee of SSc DU experts and weighted on a clinical basis. Face validity was examined by consensus and partial construct validity was tested through convergent correlation with other measures of hand function, using Spearman’s correlations. A range of patients with SSc was examined. A linear regression model with backward stepwise analysis was used to determine the relationship of individual variables with the primary clinical parameter, phyGDU. Results. Forty-four patients with SSc (9 males, mean age 55 ± 15 yrs, mean disease duration 9.9 ± 5.8 yrs) were enrolled in the study. Overall DUCAS showed significant positive correlations with all abovementioned PRO (r > 0.4, p < 0.01). When all scores and scales were modeled, only DUCAS significantly predicted phyGDU (r = 0.59, R2 = 0.354, Akaike information criterion = 385.4). Conclusion. Preliminarily, we suggest that the DUCAS may be a new clinical score for SSc-related DU, having face and content validity and convergent/divergent correlations (construct validity). These early data suggest that this score deserves further evaluation.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".