Accuracy and Psychometric Properties of the Brazilian Version of the Montreal Cognitive Assessment as a Brief Screening Tool for Mild Cognitive Impairment and Alzheimer’s Disease in the Initial Stages in the Elderly
Bibliographic record
Abstract
OBJECTIVE: To evaluate the applicability and the psychometric properties of Montreal Cognitive Assessment Brazilian Version (MoCA-BR) in the elderly, as well as comparing its accuracy as a tracking test for mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) with the accuracy of Mini-Mental State Examination (MMSE). METHOD: A transversal study was performed in 4 reference medical centers that care for the elderly. In all, 229 elderly participated in the study. To select the sample, the clinical history of the elderly, Pfeffer Functional Activities Questionnaire, and neuropsychological battery, apart from MMSE and MoCA-BR cognitive tests, were selected. The elderly were classified into control, MCI, and mild AD groups. RESULTS: There was a significant statistical difference between the MoCA-BR scores of the elderly and the control group, MCI, and mild AD (p < 0.001). The Cronbach alpha for MoCA-BR was 0.77, indicating a good internal consistency. The test-retest reliability was elevated, with intraclass correlation coefficient (ICC) 0.91. The inter-examiner reliability was excellent (ICC 0.96). The area under curve of the receiver operating characteristics curve was 0.95, when evaluating the ability of MoCA-BR to discriminate between the elderly with cognitive impairment and cognitively healthy elderly. CONCLUSIONS: The results of the study show that the Brazilian version of MoCA is a reliable cognitive tracking tool and is accurate for the detection of MCI and early stage AD, with good applicability on the elderly with education equal to or more than 4 years and adequate to discriminate between cognitively healthy elderly, and those with MCI and mild, proving to be superior to MMSE in tracking MCI and similar to this test when tracking mild AD.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".