Screening for Cognitive Impairment in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: We examined the association between responses on a screening questionnaire and objective performance on a computer-administered test of cognitive abilities in systemic lupus erythematosus (SLE). METHODS: The Cognitive Symptom Inventory (CSI) and Hospital Anxiety and Depression Scales (HADS) questionnaires were compared in patients with SLE or rheumatoid arthritis (RA). The Automated Neuropsychological Assessment Metrics (ANAM) was used to evaluate cognitive performance in patients with SLE. Efficiency of performance was measured by "throughput" (number of correct responses per minute) and "inverse efficiency" (response speed/proportion of correct responses). Linear regression was applied to log-transformed CSI scores to examine their associations with ANAM scores and other factors. RESULTS: Patients with SLE (n = 68) or RA (n = 33) were similar in age, sex, ethnicity, and education status (p > 0.05). Patients with SLE had higher total CSI scores (33.6 ± 10.5 vs 29.4 ± 6.8, respectively; p = 0.041) and attention/concentration subscale CSI scores (15.7 ± 5.3 vs 13.3 ± 3.4; p = 0.016) compared to patients with RA. In patients with SLE there was a positive association between CSI scores and neuropsychiatric (NP) events at the time of testing (p = 0.0006), HADS anxiety (p < 0.0001), and depression (p < 0.0001) scores. After adjustment for age, education, disease duration, and NP events at the time of testing, there was no significant association (p > 0.05) between ANAM and CSI scores in patients with SLE. The results were similar using either "throughput" or "inverse efficiency" or the number of impaired ANAM subscales after adjustment for simple reaction time. CONCLUSION: The CSI self-report questionnaire of cognitive symptoms does not reliably screen for efficiency of cognitive processing in patients with SLE. Rather, cognitive complaints reported in the CSI are influenced by the presence of anxiety and depression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".