Validación del instrumento Montreal Cognitive Assessment en español en adultos mayores de 60 años
Bibliographic record
Abstract
INTRODUCTION: Few studies have validated the Spanish-language version of the Montreal Cognitive Assessment (MoCA-S) test in Latin American populations. OBJETIVE: To evaluate the psychometric properties and discriminant validity of the MoCA-S in elderly patients in Santiago de Chile. METHODS: 172 individuals were grouped according to their clinical diagnosis based on the Clinical Dementia Rating (CDR) scale as follows: amnestic mild cognitive impairment (aMCI; n±24), non-amnestic MCI (naMCI; n±24), mild dementia (n±20), and cognitively normal (n±104). Participants were evaluated with both the MoCA-S and the Mini-Mental State Examination (MMSE) to determine the discriminant validity of the MoCA-S. RESULTS: Mean age and years of schooling were 73±6 and 11±4 years, respectively, with no significant intergroup differences. The MoCA-S displayed good internal consistency (Cronbach's α: 0.772), high inter-rater reliability (Spearman correlation coefficient: 0.846; P<.01), and high intra-rater reliability (test-retest reliability coefficient: 0.922; P<.001). The MoCA-S was found to be an effective and valid test for detecting aMCI (AUC±0.903) and mild dementia (AUC±0.957); its effectiveness for detecting naMCI was lower (AUC±0.629). The optimal cut-off points for aMCI and mild dementia were<21 and<20, respectively, with sensitivity and specificity rates of 75% and 82% for aMCI and 90% and 86% for mild dementia. The level of education had a great impact on scores: as a result, 2 points were added for patients with less than 8 years of schooling and one point for patients with 8-12 years of schooling (MoCA-S1-2). The MoCA-S1-2 showed significantly greater discriminant validity than the MMSE for differentiating aMCI from dementia. CONCLUSIONS: The MoCA-S1-2 is a short, easy-to-use, and useful test for diagnosing aMCI and mild dementia.
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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 | 0.006 | 0.011 |
| 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 | 0.000 |
| 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".