Assessing the Utility of the Montreal Cognitive Assessment in Screening for Cognitive Impairment in Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Screening for cognitive impairment (CI) in systemic lupus erythematosus (SLE) relies on the American College of Rheumatology (ACR) neuropsychological battery (NB). By studying the concurrent criterion validity, our goal was to assess the Montreal Cognitive Assessment (MoCA) as a screening tool for CI compared to the ACR-NB and to evaluate the added value of the MoCA to the Automated Neuropsychological Assessment Metrics (ANAM). METHODS: A total of 285 adult SLE patients were administered the ACR-NB, MoCA, and ANAM. For the ACR-NB, patients were classified as having CI if there was a Z score of ≤-1.5 in ≥2 domains. The area under the curve (AUC) and sensitivities/specificities were determined. A discriminant function analysis was applied to assess the ability of the MoCA to differentiate between CI, undetermined CI, and non-CI patients. RESULTS: CI was not accurately identified by the MoCA compared to the ACR-NB (AUC of 0.66). Sensitivity and specificity were poor at 50% and 69%, respectively, for the cutoff of 26, and 80% and 45%, respectively, for the cutoff of 28. The MoCA had a low ability to identify CI status. The addition of the MoCA to the ANAM led to improvement on the AUC by only 2.5%. CONCLUSION: The MoCA does not have adequate concurrent criterion validity to accurately identify CI in patients with SLE. The low specificity of the MoCA may lead to overdiagnosis and concern among patients. Adding the MoCA to the ANAM does not substantially improve the accuracy of the ANAM. These results do not support using the MoCA as a screening tool for CI in patients with 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".