Psychometric Evaluation of the Scleroderma Skin Questionnaire: A Novel Patient-Reported Outcome for Skin Disease in Patients With Systemic Sclerosis
Bibliographic record
Abstract
Objective We aimed to evaluate the psychometric properties of the Scleroderma Skin Questionnaire (SSQ), a novel patient-reported outcome (PRO) to assess systemic sclerosis (SSc)–related skin symptoms. Methods Participants were recruited from the SSc Collaborative National Quality and Efficacy Registry (CONQUER). Internal consistency was determined using Cronbach α and McDonald ω total (ωt). The correlation of the SSQ was assessed with the modified Rodnan skin score (mRSS), physician global assessment (PGA), Scleroderma Health Assessment Questionnaire, 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29), and patient global assessment to assess criterion, convergent, and divergent validity. Correlations were also assessed between patients’ self-reported recall of skin changes over the past 6 months (“SSQ 6-Month”) and 6-month change in mRSS. Results The SSQ was administered to 799 adults (mean age 52.7; 83% female) enrolled in CONQUER. Cronbach α was 0.90 and ωtwas 0.92, indicating high internal consistency. The SSQ was moderately correlated with mRSS (r0.56), with stronger correlations in diffuse (r0.54) vs limited cutaneous subtypes (r0.24; allP< 0.05). The SSQ was also moderately-to-strongly correlated with PROMIS-29 physical function (r−0.50) and pain interference subscales (r0.61), strongly with Health Assessment Questionnaire score (r0.63) and severity subscale (r0.62), and moderately with PGA SSc activity score (r0.48; allP< 0.05). SSQ 6-Month correlated weakly with the 6-month change in mRSS (r0.26;P< 0.05). Conclusion SSQ demonstrated high reliability and moderate correlation with mRSS and legacy PROs. This study provides initial support for SSQ, but not SSQ 6-Month, to assess skin symptoms in patients with SSc.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".