Assessment of cognitive function in systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis by computerized neuropsychological tests
Bibliographic record
Abstract
OBJECTIVE: Computerized neuropsychological testing may facilitate screening for cognitive impairment in systemic lupus erythematosus (SLE). This study was undertaken to compare patients with SLE, patients with rheumatoid arthritis (RA), and patients with multiple sclerosis (MS) with healthy controls using the Automated Neuropsychological Assessment Metrics (ANAM). METHODS: Patients with SLE (n = 68), RA (n = 33), and MS (n = 20) were compared with healthy controls (n = 29). Efficiency of cognitive performance on 8 ANAM subtests was examined using throughput (TP), inverse efficiency (IE), and adjusted IE scores. The latter is more sensitive to higher cognitive functions because it adjusts for the impact of simple reaction time on performance. The results were analyzed using O'Brien's generalized least squares test. RESULTS: Control subjects were the most efficient in cognitive performance. MS patients were least efficient overall (as assessed by TP and IE scores) and were less efficient than both SLE patients (P = 0.01) and RA patients (P < 0.01), who did not differ. Adjusted IE scores were similar between SLE patients, RA patients, and controls, reflecting the impact of simple reaction time on cognitive performance. Thus, 50% of SLE patients, 61% of RA patients, and 75% of MS patients had impaired performance on >or=1 ANAM subtest. Only 9% of RA patients and 11% of SLE patients had impaired performance on >or=4 subtests, whereas this was true for 20% of MS patients. CONCLUSION: ANAM is sensitive to cognitive impairment. While such computerized testing may be a valuable screening tool, our results emphasize the lack of specificity of slowed performance as a reliable indicator of impairment of higher cognitive function in SLE patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".