Neuropsychologic Functioning and Health Status in Systemic Lupus Erythematosus
Bibliographic record
Abstract
BACKGROUND: Despite increased severity of lupus in blacks, including more frequent neuropsychiatric manifestations, there is sparse data on neuropsychologic function in black patients with lupus. METHODS: Neuropsychologic functioning and health-related variables were examined among blacks (n = 34) and whites (n = 14) fulfilling American College of Rheumatology criteria for systemic lupus erythematosus. RESULTS: Blacks and whites performed comparably on measures of verbal and visual memory, working memory, and motor speed after controlling for estimates of premorbid cognitive ability. Blacks trended towards poorer performance on specific attention/processing speed measures. Pain, fatigue, depression, anxiety, physical and emotional well-being were unrelated to ethnicity. Blacks exhibited a trend towards greater impairment of physical functioning. Ethnicity-related differences in overall damage, noncognitive neuropsychiatric manifestations, and prevalence of nephritis revealed greater severity among blacks. CONCLUSIONS: Initial differences in premorbid cognitive function possibly contribute to disparate clinical outcomes, including a greater proportion of blacks exhibiting subnormal neurocognitive performance. Blacks evidencing lower premorbid ability may be at greater vulnerability for poorer functional outcomes (eg, coping skills, medical compliance and employment) if they experience disease-related cognitive dysfunction.
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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) | 0.002 | 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".