Major Depression Diagnoses Among Patients With Systemic Sclerosis: Baseline and One‐Month Followup
Bibliographic record
Abstract
OBJECTIVE: Depression is common in many rheumatic diseases and is associated with poor prognosis. No studies of patients with any rheumatic diseases, however, have assessed the stability of major depressive disorder (MDD) diagnoses over time. The objective of the present study was to assess the stability of MDD diagnoses among patients with systemic sclerosis (SSc; scleroderma), a rare autoimmune rheumatic disease, across 2 assessments approximately 1 month apart. METHODS: SSc patients were recruited from 7 Canadian Scleroderma Research Group Registry sites (April 2009 to June 2012). Current (30-day) MDD was assessed with the Composite International Diagnostic Interview at baseline and approximately 1 month later. RESULTS: Among 309 patients with baseline assessments who received followup assessments an average of 34 days later, prevalence of 30-day MDD was 4% (95% confidence interval [95% CI] 2%-7%; n = 12) at baseline and 5% (95% CI 3%-8%; n = 16) at followup. Only 3 of 12 patients (25% [95% CI 9%-53%]) with MDD at baseline had MDD 1 month later. CONCLUSION: Most patients with SSc who meet criteria for MDD appear to experience mild, time-limited episodes of low mood that often resolve on their own without specific treatment. Consistent with international guidelines on depression management in nonpsychiatric settings, "watchful waiting" or "active monitoring" is a good strategy for SSc patients with mild depression to avoid unnecessary treatment among those whose symptoms may be transient and may resolve without medical intervention.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".