Cognitive dysfunction in SLE: development of a screening tool
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction (CD) is among the most common neuropsychiatric manifestations of systemic lupus erythematosus (SLE). There are two methods which have been used to detect CD in patients with SLE: traditional neuropsychological tests (NPT) and the Automated Neuropsychological Assessment Metrics (ANAM). Both are time-consuming and neither is readily available for screening purposes. PURPOSE: The aim of our study was to evaluate the Montreal Cognitive Assessment (MoCA) test as a screening tool for detection of CD in SLE. Methods. SLE patients fulfilling ACR criteria were administered the ANAM, a computerized test battery which measures various cognitive domains and the MoCA, a one-page, performance-based screening test designed to detect mild cognitive impairment in the elderly. With the ANAM as the gold standard, the performance characteristics of the MoCA were assessed. RESULTS: In total, 44 patients were evaluated. Of these, 11 (25%) were identified by the ANAM as being impaired in comparison with 13 (29.5%) by the MoCA. The scores were significantly correlated (r = 0.57, p < 0.001). Using the standard cutoff of 26, the sensitivity of MoCA was 83% and specificity 73%. CONCLUSION: The MoCA appears to be a promising screening tool for the detection of CD in SLE both for epidemiologic studies and for routine clinical care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".