Clinically Recognized Depression and Mental Health Treatment in a Single Center Cohort of Patients with Systemic Sclerosis
Bibliographic record
Abstract
Introduction: In this study, we investigated the prevalence of depression, depression treatment, and symptom burden in patients with systemic sclerosis (SSc) and examined their associations with the center for epidemiologic studies depression scale revised (CESD-R) scores. Methods: The Prospective Registry in Scleroderma at Massachusetts General Hospital (PRISM) is a longitudinal registry of patients with SSc. Among participants with CESD-R score ≥ 16, indicating possible depression, a chart review was performed for mental health diagnoses and treatments. We examined the relation of demographic and clinical factors to the presence of mental health diagnoses or treatment using logistic regression. We evaluated the association of SSc symptoms and the COVID-19 pandemic with a CESD-R score using quantile regression. Results: Of 214 patients enrolled in PRISM, 129 participants (38% diffuse and 59% limited) completed at least one CESD-R questionnaire. In the first survey, 29% had possible depression (CESD - R ≥ 16) and 16% had probable depression (CESD - R ≥ 23). Of 20 participants with probable depression, 90% received treatment for a mood disorder. In a multivariable logistic regression model among participants with CESD - R ≥ 16, none of the evaluated variables (CESD-R score, age, gender, employment status, race, and ethnicity) was associated with mental health diagnosis or treatment. Higher baseline dyspnea index, modified Rodnan skin score, and the University of California Los Angeles Scleroderma Clinical Trials Consortium Gastrointestinal total score and subscores were associated with higher CESD-R score. Conclusion: In this single-center cross-sectional study, 16% of participants had significant depressive symptoms. Dyspnea, extent of skin involvement, and gastrointestinal symptoms were associated with depression symptoms.
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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 | 0.001 | 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".