Sociodemographic and Disease Correlates of Body Image Distress among Patients with Systemic Sclerosis
Bibliographic record
Abstract
BACKGROUND: Body image concerns are infrequently studied in systemic sclerosis (SSc), even though significant visible disfigurement is common. The objective of this study was to identify sociodemographic and disease-related correlates of dissatisfaction with appearance and social discomfort among people with SSc. METHODS: SSc patients came from the 15-center Canadian Scleroderma Research Group Registry. Sociodemographic information was based on patient self-report. Disease characteristics were obtained via physician examinations. The Brief-SWAP was used to assess dissatisfaction with appearance and social discomfort. Structural equation models were conducted with MPlus to determine the relationship of dissatisfaction with appearance and social discomfort with age, sex, education, marital status, race/ethnicity, disease duration, skin involvement, telangiectasias, skin pigmentation changes, and hand contractures. RESULTS: A total of 489 SSc patients (432 female, 57 male) were included. Extent of skin involvement was significantly associated with both dissatisfaction with appearance and social discomfort (standardized regression coefficients = 0.02, p = 0.001; 0.02, p = 0.020, respectively), as was skin involvement in the face (0.18, p = 0.016; 0.23, p = 0.006, respectively). Greater social discomfort was robustly associated with younger age (-0.017, p<0.001) and upper-body telangiectasias (0.32, p = 0.021). Dissatisfaction with appearance was associated with hand contractures (0.07, p = 0.036). CONCLUSION: This study found that dissatisfaction with appearance and social discomfort were associated with numerous disfiguring characteristics of SSc, in addition to age. These results underline that there are multiple factors contributing to body image distress in SSc, as well as the need to attend to both disease and social contexts in understanding the impact of disfigurement among patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".