Neurocognitive Impairment in Corticosteroid-naive Patients with Active Systemic Lupus Erythematosus: A Prospective Study
Bibliographic record
Abstract
OBJECTIVE: Neurocognitive impairment (NCI) has been intensively studied in patients with systemic lupus erythematosus (SLE). However, those studies have mostly included patients who were treated with corticosteroids, which may itself induce NCI. We investigated NCI in corticosteroid-naive people with SLE who did not exhibit any overt neuropsychiatric manifestations. METHODS: Forty-three inpatients with SLE who had no current or past neuropsychiatric history participated in the study. Patients and 30 healthy control subjects with similar demographic characteristics were given a 1-h battery of neuropsychological tests. NCI was defined as scores at least 2 SD below the mean of the healthy control group on at least 2 of the 7 neurocognitive domains. Results of clinical, laboratory, and neurologic tests were compared regarding the presence of NCI. RESULTS: NCI was identified in 12 patients (27.9%) with SLE and in 2 control subjects (6.7%). Patients with SLE showed a significant impairment compared with controls on tasks assessing immediate recall, complex attention/executive function, and psychomotor speed. We identified psychomotor speed (Digit Symbol Substitution Test) as the factor that best differentiated the 2 groups. Further, we identified the score of the SLE Disease Activity Index 2000 as an independent risk factor for NCI in patients with SLE. CONCLUSION: We conclude that reduced psychomotor speed is an SLE-specific pattern of NCI. Verbal-memory deficits that have been reported in patients with SLE were not evident among patients who were corticosteroid-naive. Our results indicate that impaired psychomotor speed may be added to the symptoms of early 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".