Trajectories of depressive symptoms in systemic lupus erythematosus over time
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to determine the trajectories of depressive symptoms in patients with SLE and to identify baseline characteristics that are associated with a patient's trajectory of depression. METHODS: Data from the Lupus Outcomes Study at the University of California, San Francisco were analysed. Depressive symptomatology was assessed in years two through seven using the Center for Epidemiologic Studies Depression Scale (CES-D), with higher scores representing more severe depressive symptoms. Group-based trajectory modelling was used to determine latent classes of CES-D scores over time. Ordinal logistic regression analyses were performed to identify baseline characteristics associated with worse classes of depressive symptoms. RESULTS: CES-D scores for 763 individuals with SLE over 6 years were mapped into four distinct classes. Class 1 (36%) and class 2 (32%) comprised the largest proportion of the cohort and were defined by the lowest and low CES-D scores (no depression), respectively. Class 3 (22%) and class 4 (10%) had high and the highest scores (depression), respectively. Greater age [odds ratio (OR): 0.97, 95% CI: 0.96, 0.99] and higher education level (OR: 0.79, 95% CI: 0.70, 0.89) at baseline were associated with lower odds of membership in worse classes of depressive symptoms. Conversely, lower income (OR: 1.73, 95% CI: 1.03, 2.92), worse SF-36 physical functioning scores (OR: 1.12, 95% CI: 1.12, 1.13) and worse SF-36 bodily pain scores (OR: 1.58, 95% CI: 1.55, 1.61) were positively associated with membership in worse classes of depressive symptoms. CONCLUSION: Four classes of depressive symptoms were identified in patients with SLE. Understanding the trajectories of depressive symptoms and the associated risk factors can aid in the management of these symptoms in individuals living with 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 | 0.004 |
| 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 | 0.001 | 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".