The association of resilience and positive mental health in systemic sclerosis: A Scleroderma Patient-centered Intervention Network (SPIN) cohort cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: A previous study using Scleroderma Patient-centered Intervention Network (SPIN) Cohort data identified five classes of people with systemic sclerosis (also known as scleroderma) based on patient-reported somatic (fatigue, pain, sleep) and mental health (anxiety, depression) symptoms and compared indicators of disease severity between classes. Across four classes ("low", "normal", "high", "very high"), there were progressively worse somatic and mental health outcomes and greater disease severity. The fifth ("high/low") class, however, was characterized by high disease severity, fatigue, pain, and sleep but low mental health symptoms. We evaluated resilience across classes and compared resilience between classes. METHODS: Cross-sectional study. SPIN Cohort participants completed the 10-item Connor-Davidson-Resilience Scale (CD-RISC) and PROMIS v2.0 domains between August 2022 and January 2023. We used latent profile modeling to identify five classes as in the previous study and multiple linear regression to compare resilience levels across classes, controlling for sociodemographic and disease variables. RESULTS: Mean CD-RISC score (N = 1054 participants) was 27.7 (standard deviation = 7.3). Resilience decreased progressively across "low" to "normal" to "high" to "very high" classes (mean 4.7 points per step). Based on multiple regression, the "high/low" class exhibited higher resilience scores than the "high" class (6.0 points, 95% confidence interval [CI] 4.9 to 7.1 points; standardized mean difference = 0.83, 95% CI 0.67 to 0.98). CONCLUSIONS: People with worse disease severity and patient-reported outcomes reported substantially lower resilience, except a class of people with high disease severity, fatigue, pain, and sleep disturbance but positive mental health and high resilience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".