Examination of the association of sex and race/ethnicity with appearance concerns: a Scleroderma Patient-centered Intervention Network (SPIN) Cohort study.
Bibliographic record
Abstract
OBJECTIVES: Appearance concerns are common in systemic sclerosis (SSc) and have been linked to younger age and more severe disease. No study has examined their association with sex or race/ethnicity. METHODS: SSc patients were sampled from the Scleroderma Patient-centered Intervention Network Cohort. Presence of appearance concerns was assessed with a single item, and medical and sociodemographic information were collected. RESULTS: Of 644 patients, appearance concerns were present in 72%, including 421 of 565 women (75%), 42 of 79 men (53%), 392 of 550 patients who identified as White (71%), 35 of 41 who identified as Black (85%), and 36 of 53 who identified as another race/ethnicity (68%). In multivariate analysis, women had significantly greater odds of reporting appearance concerns than men (odds ratio (OR)=2.97, 95% confidence interval (CI)=1.78-4.95, p<.001). Black patients had significantly greater odds of appearance concerns than White patients in unadjusted (OR=2.64, 95% CI=1.01-6.34, p=.030), but not multivariate analysis (OR=1.76, 95% CI=0.67-4.60, p=.250). Compared to a general population sample, appearance concerns were substantially more common in SSc, particularly for men across all age groups and for younger women. The most commonly reported features of concern were related to the face and head, followed by the hands and fingers; this did not differ by sex or race/ethnicity. CONCLUSIONS: Appearance concerns were common in SSc. Women were substantially more likely than men to have appearance concerns. Although non-significant in multivariate analysis, Black patients were more likely to have concerns than White patients, likely due to more severe changes in appearance.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".