Factor structure and convergent validity of the Derriford Appearance Scale-24 using standard scoring versus treating ‘not applicable’ responses as missing data: a Scleroderma Patient-centered Intervention Network (SPIN) cohort study
Bibliographic record
Abstract
OBJECTIVE: Valid measures of appearance concern are needed in systemic sclerosis (SSc), a rare, disfiguring autoimmune disease. The Derriford Appearance Scale-24 (DAS-24) assesses appearance-related distress related to visible differences. There is uncertainty regarding its factor structure, possibly due to its scoring method. DESIGN: Cross-sectional survey. SETTING: Participants with SSc were recruited from 27 centres in Canada, the USA and the UK. Participants who self-identified as having visible differences were recruited from community and clinical settings in the UK. PARTICIPANTS: Two samples were analysed (n=950 participants with SSc; n=1265 participants with visible differences). PRIMARY AND SECONDARY OUTCOME MEASURES: The DAS-24 factor structure was evaluated using two scoring methods. Convergent validity was evaluated with measures of social interaction anxiety, depression, fear of negative evaluation, social discomfort and dissatisfaction with appearance. RESULTS: When items marked by respondents as 'not applicable' were scored as 0, per standard DAS-24 scoring, a one-factor model fit poorly; when treated as missing data, the one-factor model fit well. Convergent validity analyses revealed strong correlations that were similar across scoring methods. CONCLUSIONS: Treating 'not applicable' responses as missing improved the measurement model, but did not substantively influence practical inferences that can be drawn from DAS-24 scores. Indications of item redundancy and poorly performing items suggest that the DAS-24 could be improved and potentially shortened.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".