Validation of the Montreal Cognitive Assessment (MoCA) in Spanish as a screening tool for mild cognitive impairment and mild dementia in patients over 65 years old in Bogotá, Colombia
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) was developed as a simple screening tool for cognitive impairment. This study is the first validation in Latin America of the MoCA in Spanish (MoCA-S), which was developed in Colombia (South America). METHODS: Aiming to perform the first validation of the MoCA-S, we developed a study of concordance by conformity to assess the MoCA-S compared with diagnostic consensus by interdisciplinary assessment in the Memory Clinic (the best diagnostic method available) and to evaluate the psychometric properties of the MoCA-S. A total of 193 subjects were evaluated, 109 of whom were patients, including 26 who met the mild cognitive impairment (MCI) clinical criteria, based on neuropsychological testing, and 83 who had mild dementia (MD). The remaining 84 participants were healthy subjects from the community. RESULTS: The psychometric evaluation of the MoCA-S was appropriate. Using a cutoff score of ≥ 23, the MoCA had sensitivities of 76.0% to detect MCI and 92.7% to detect MD and a specificity of 79.8%. The percentage of patients clearly labeled by the MoCA-S was 85%. CONCLUSION: The MoCA-S is a valid screening tool and is useful for identifying MCI and MD in Colombia. The MoCA-S is valid and adequate for application in Colombia with good internal consistency, inter-observer reliability, and content validity. However, the average educational level was high in this study; thus, caution should be exercised when extrapolating these results to individuals with lower educational levels.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".