The Montreal Cognitive Assessment Test
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Systemic lupus erythematosus (SLE) is an inflammatory, chronic, and multisystemic disease, which may be associated with a wide range of neuropsychiatric manifestations, including cognitive impairment. Cognitive evaluations based on screening tests might identify early SLE-related cognitive alterations. The aim of this study was to evaluate and to compare the efficacy of three screening tests (Montreal Cognitive Assessment [MoCA], Mini Mental State Examination [MMSE], Cognitive Symptom Inventory [CSI]) against the gold standard (neuropsychological battery), in order to identify the most efficient screening test for cognitive impairment in patients with SLE. METHODS: This observational cross-sectional study recruited 44 patients, from August to December 2017, who were diagnosed with SLE according to the Systemic Lupus International Collaborating Clinics (SLICC) Criteria 2012, and had no medical or psychiatric comorbidities. The patients were evaluated using the MoCA, MMSE, CSI, and the gold standard. Spearman's correlation and area under the curve analysis were performed; p < 0.05 was considered significant. RESULTS: The MoCA test showed the highest correspondence with the gold standard (AUC = 99.4%, p < 0.001), sensitivity (84%), and specificity (100%). This was followed by the MMSE (AUC = 92.6%, p < 0.001; sensitivity, 54.8%; specificity, 100%) and the CSI (AUC = 30.6%, p < 0.05; sensitivity, 54.8%; specificity, 30.76%). CONCLUSION: The MoCA is a brief, easily applied screening test that is highly effective for detecting cognitive impairment in SLE patients. It could be useful in clinical follow-up as a tool for early detection of cognitive alterations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.003 |
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".