Prevalence and clinical correlates of symptoms of depression in patients with systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and predictors of symptoms of depression in a large sample of patients with systemic sclerosis (SSc). METHODS: We conducted a cross-sectional, multicenter study of 376 patients with SSc from the Canadian Scleroderma Research Group Registry. Patients were assessed with the Center for Epidemiologic Studies Depression Scale (CES-D) and through extensive clinical histories and medical examinations. Hierarchical multiple linear regression was used to assess the relationship of sociodemographic and clinical variables with symptoms of depression. RESULTS: The percentages of patients who scored > or =16 and > or =23 on the CES-D were 35.1% and 18.1%, respectively. Patients with less education; patients who were not married; patients with higher physician-rated overall disease severity; and patients with more tender joints, more gastrointestinal symptoms, and more difficulty breathing had significantly higher total CES-D scores. As a group, specific symptom indicators (tender joints, gastrointestinal symptoms, breathing) predicted the most incremental variance in depressive symptoms (DeltaR(2) = 14.2%, P < 0.001) despite being added to the model after demographic, socioeconomic, and global disease duration/severity indicators. CONCLUSION: High levels of depressive symptoms are common in patients with SSc and are related to overall SSc disease severity, as well as specific medical symptoms. Screening for depression among patients with SSc is recommended, although more research is needed to determine the best method for doing this. Successfully treating dyspnea, gastrointestinal symptoms, and joint pain may improve mood, although this has not yet been demonstrated.
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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) | 0.001 | 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".