Evaluation of predictive factors for neurocognitive dysfunction in patients with inactive systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: To determine predictive factors associated with the cognitive dysfunction in patients with inactive systemic lupus erythematosus (SLE). METHODS: Consecutive patients followed at the Lupus Clinic with inactive SLE (SLE Disease Activity Index, SLEDAI, = 0) underwent a battery of neuropsychological tests; Beck Depression Inventory and psychiatric assessment were also performed. Neurocognitive dysfunction was defined as 3 abnormal scores. Data were analyzed using chi-square tests, ANOVA tests, and logistic regression. RESULTS: Twenty-five of the 58 patients with SLE (43%) versus 9 of 47 healthy controls (19%) demonstrated neurocognitive dysfunction (p < 0.01). Neurocognitive dysfunction was not associated with depression or a psychiatric diagnosis, use of steroids, or previous or current evidence for fibromyalgia. SLEDAI > 10 at first presentation to the Lupus Clinic and previous vasculitis were associated with neurocognitive dysfunction, but previous central nervous system disease, renal disease, renal damage, or atherosclerotic complications were not. Neurophysiologic studies at the time of the assessment were not predictive of neurocognitive dysfunction. CONCLUSION: Patients with inactive SLE demonstrate neurocognitive dysfunction. This is associated with more disease activity at presentation, but is not associated with specific organ involvement or organ damage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".