Validation of the Social Appearance Anxiety Scale in Patients With Systemic Sclerosis: A Scleroderma Patient‐Centered Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is an autoimmune disease that can cause disfiguring changes in appearance. This study examined the structural validity, internal consistency reliability, convergent validity, and measurement equivalence of the Social Appearance Anxiety Scale (SAAS) across SSc disease subtypes. METHODS: Patients enrolled in the Scleroderma Patient-centered Intervention Network Cohort completed the SAAS and measures of appearance-related concerns and psychological distress. Confirmatory factor analysis (CFA) was used to examine the structural validity of the SAAS. Multiple-group CFA was used to determine whether SAAS scores can be compared across patients with limited and diffuse disease subtypes. Cronbach's alpha was used to examine internal consistency reliability. Correlations of SAAS scores with measures of body image dissatisfaction, fear of negative evaluation, social anxiety, and depression were used to examine convergent validity. SAAS scores were hypothesized to be positively associated with all convergent validity measures, with correlations significant and moderate to large in size. RESULTS: A total of 938 patients with SSc were included. CFA supported a 1-factor structure (Comparative Fit Index 0.92, Standardized Root Mean Residual 0.04, and Root Mean Square Error of Approximation 0.08), and multiple-group CFA indicated that the scalar invariance model best fit the data. Internal consistency reliability was good in the total sample (α = 0.96) and in disease subgroups. Overall, evidence of convergent validity was found with measures of body image dissatisfaction, fear of negative evaluation, social anxiety, and depression. CONCLUSION: The SAAS can be reliably and validly used to assess fear of appearance evaluation in patients with SSc, and SAAS scores can be meaningfully compared across disease subtypes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".