Validation of the Argentine version of the Montreal Cognitive Assessment Test (MOCA): A screening tool for Mild Cognitive Impairment and Mild Dementia in Elderly
Bibliographic record
Abstract
The MoCA is a brief useful test to diagnose mild cognitive impairment (MCI) and mild dementia (MD). To date, no Argentine cross-cultural adapted validations of the Spanish version have been reported. OBJECTIVE: To validate the MoCA in the elderly and study its usefulness in MCI and MD. METHODS: This study included 399 individuals over 60 years old evaluated in the Cognitive-Behavioral Department (2017-2018). Patients with<3 years of education, sensory disturbances, psychiatric disorders, or moderate-severe dementia were excluded. The control group comprised cognitively normal subjects. Participants were classified according to neuropsychological assessment and clinical standard criteria into Control, MCI or MD groups. A locally adapted MoCA (MOCA-A) was administered to the patients and controls. RESULTS: Mean educational level was 10.34 years (SD 3.5 years). MoCA-A score differed significantly among groups (p<0.0001). MoCA-A performance correlated with educational level (r: 0.406 p<0.00001). Adopting a cut-off score ≥25 (YI=0.55), the sensitivity for MCI was 84.8% and for MD 100%, with specificity of 69.7%. When adding a single point to the score in patients with ≤12 years of education, the specificity of the test reached 81%. CONCLUSION: The MoCA-A is an accurate reliable screening test for MCI and MD in Argentina.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".