Cognitive Function Trajectories in Association With the Depressive Symptoms Trajectories in Systemic Lupus Erythematosus Over Time
Bibliographic record
Abstract
OBJECTIVE: Cognitive function may change over time in patients with systemic lupus erythematosus (SLE), and cognitive function trajectories have not been well studied. We aimed to identify cognitive function trajectories in SLE and describe them with depressive symptoms trajectories, and we also aimed to identify baseline factors associated with class membership in the dual trajectories. METHODS: Longitudinal data from the University of California San Francisco Lupus Outcomes Study were analyzed. Two outcome trajectories were studied jointly, the Hopkins Verbal Learning Test-Revised and the Center for Epidemiologic Studies Depression Scale (CES-D) (administered annually). Univariate/multivariable logistic regression analyses examined baseline factors associated with class memberships. RESULTS: A total of 755 patients were studied, and 4 latent classes were identified: 1) low CES-D scores and low cognitive scores (no depression plus cognitive impairment; 20%), 2) lowest CES-D scores and highest normal cognitive scores (no depression plus normal cognition; 48%), 3) highest CES-D scores and lowest cognitive scores (depression plus cognitive impairment; 9%), and 4) high CES-D scores and cognitive score at borderline (depression plus borderline cognition; 23%). CONCLUSION: In all, 4 distinct classes of dual cognitive function and depressive symptoms were identified. Persistently low cognitive performance in 28% of patients (classes 1 and 3) did not significantly improve over 7 years. Cognitive impairment was associated with depression status in 9% of patients (class 3). Other factors also predicted latent class membership: ethnicity, education, disease activity, physical functioning, and bodily pain. These results highlight the importance of periodic assessment of cognitive function and of different aspects relevant for assessing and managing cognitive function over time 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 | 0.004 |
| 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".