Shortening patient-reported outcome measures through optimal test assembly: application to the Social Appearance Anxiety Scale in the Scleroderma Patient-centered Intervention Network Cohort
Bibliographic record
Abstract
OBJECTIVES: The Social Appearance Anxiety Scale (SAAS) is a 16-item measure that assesses social anxiety in situations where appearance is evaluated. The objective was to use optimal test assembly (OTA) methods to develop and validate a short-form SAAS based on objective and reproducible criteria. DESIGN: This study was a cross-sectional analysis of baseline data from adults enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort. SETTING: Adults in the SPIN Cohort in the present study were enrolled at 28 centres in Canada, the USA and the UK. PARTICIPANTS: The SAAS was administered to 926 adults with scleroderma. PRIMARY AND SECONDARY MEASURES: The SAAS, Brief Fear of Negative Evaluation II (BFNE II), Brief Satisfaction with Appearance Scale (Brief-SWAP), Patient Health Questionnaire-8 (PHQ8) and Social Interaction Anxiety Scale-6 (SIAS-6) were collected, as well as demographic characteristics. RESULTS: OTA methods identified a maximally informative shortened version for each possible form length between 1 and 15 items. The final shortened version was selected based on prespecified criteria for reliability, concurrent validity and statistically equivalent convergent validity with the BFNE II scale. A five-item short version was selected (SAAS-5). The SAAS-5 had a Cronbach's α of 0.95 and had high concurrent validity with the full-length form (r=0.97). The correlation of the SAAS-5 with the BFNE II was 0.66, which was statistically equivalent to that of the full-length form. Furthermore, the correlation of the SAAS-5 with the two subscales of the Brief-SWAP, and the SIAS-6, were statistically equivalent to that of the full-length form. CONCLUSIONS: OTA was an efficient method for shortening the full-length SAAS to create the SAAS-5.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 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".