Predictors of Incident Depression in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Findings from previous studies of predictors of depression among patients with systemic lupus erythematosus (SLE) have been inconsistent. The aim of our study was to identify risk factors that preceded incident depression based on a large, closely followed longitudinal cohort. METHODS: Data regarding 1609 patients with SLE in the Hopkins Lupus Cohort who had no history of depression prior to cohort entry were analyzed. Demographic variables, SLE manifestations, laboratory tests, physician's global assessment, Safety of Estrogens in Lupus Erythematosus National Assessment-SLE Disease Activity Index (SELENA-SLEDAI), cumulative organ damage (Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index), and onset of depression were recorded at enrollment and each quarterly visit. Rates of incident depression were calculated overall, and in subgroups defined by demographic and clinical variables. Adjusted estimates of association were derived using pooled logistic regression. RESULTS: The incidence of depression was 29.7 episodes per 1000 person-years. In the multivariable analysis, these variables remained as independent predictors of incident depression: recent SLE diagnosis, non-Asian ethnicity, disability, cutaneous activity, longitudinal myelitis, and current prednisone use of 20 mg/day or higher. Global disease activity (SELENA-SLEDAI) was not a significant predictor after controlling for prednisone use. CONCLUSION: Depression in SLE is multifactorial. Higher-dose prednisone (≥ 20 mg daily) is 1 important independent risk factor. Global disease activity is not a risk factor, but cutaneous activity and certain types of neurologic activity (myelitis) are predictive of depression. The independent effect of prednisone provides clinicians with an additional incentive to avoid and reduce high-dose prednisone exposure in SLE.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".