Screening for cognitive dysfunction in systemic lupus erythematosus: the Montreal Cognitive Assessment Questionnaire and the Informant Questionnaire on Cognitive Decline in the Elderly
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction (CD) is among the most common neuropsychiatric manifestations of systemic lupus erythematosus (SLE). Traditional neuropsychological testing and the Automated Neuropsychologic Assessment Metrics (ANAM) have been used to assess CD but neither is an ideal screening test. The Montreal Cognitive Assessment Questionnaire (MoCA) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) are brief and inexpensive tests. This study evaluated the MoCA and IQCODE as screening tools. METHODS: SLE patients fulfilling American College of Rheumatology (ACR) classification criteria were evaluated using the ANAM as the reference standard. The performance characteristics of the MoCA and IQCODE were assessed in comparison with normal controls (NCs) and rheumatoid arthritis (RA) patients. Four different definitions of CD were utilized. RESULTS: In total, 78 patients were evaluated. MoCA and ANAM scores were significantly correlated ( r = 0.51, p < 0.001). At the optimal cutoff, the sensitivity of the MoCA was ≥ 90% (depending on definition of CD) vs RA patients and ≥83% vs NCs. ANAM and IQCODE scores did not correlate ( p = 0.8152). IQCODE sensitivities were low for both RA patients and NCs regardless of definition and cutoff used. CONCLUSION: The MoCA appears to be a promising and practical screening tool for identification of patients with SLE at risk for CD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".