Patient Acceptable Symptom State for Burden From Appearance Changes in People With Systemic Sclerosis: A Cross-sectional Survey
Bibliographic record
Abstract
OBJECTIVE: People with systemic sclerosis (SSc) often report substantial burden from appearance changes. We aimed to estimate the patient acceptable symptom state (PASS) for burden from appearance changes in people with SSc. METHODS: We conducted a secondary analysis of the SCISCIF II study, a cross-sectional survey of 113 patients with SSc from France enrolled in the Scleroderma Patient-centered Intervention Network Cohort. Burden from appearance changes was assessed with a self-administered numeric rating scale (0, no burden to 10, maximal burden). Acceptability of the symptom state was assessed with a specific anchoring question. Participants who answered yes were in the group of patients who considered their symptom state as acceptable. The PASS for the burden from appearance changes was estimated with the 75th percentile method. RESULTS: Assessments of burden from appearance changes and answers to the anchoring question were available in 82/113 (73%) participants from the SCISCIF II study. Median age was 55 (IQR 24) years, mean disease duration 9.6 (SD 6.5) years and 32/80 (40%) participants had diffuse cutaneous SSc. The PASS estimate for the burden from appearance changes was 4.8 (95% CI 1.0-7.0) of 10 points. CONCLUSION: Our study provides a PASS estimate for burden from appearance changes. Our estimate could serve as a binary response criterion to assess the efficacy of treatments targeting burden from appearance changes.
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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 | 0.003 | 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 | 0.000 | 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) | 0.002 | 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".