The Montreal Cognitive Assessment in Spanish-speaking countries in Latin America and the Caribbean: A Systematic Review
Bibliographic record
Abstract
Abstract The worldwide prevalence of individuals living with dementia is on the rise and expected to reach 152.8 million people by 2050. This increase will affect disproportionately Low- and Middle Income Countries, which is already home to 60% of individuals living with dementia. Screening tools for early detection of mild cognitive impairment are crucial to provide patients with adequate diagnosis, timely interventions, and access to disease-modifying therapies. The Montreal Cognitive Assessment (MoCA) is one of the most widely used tests for cognitive assessment of first line, but its uniform application across Latin American and Caribbean (LAC) countries is questionable due to cultural and linguistic differences, a higher prevalence of low educational achievement and socioeconomical disparities. This systematic review for validation of the MoCA in Spanish-speaking LAC countries identified fourteen studies but only included twelve based on inclusion and exclusion criteria. We assessed the articles for cultural adaptation and translation reports using the Manchester Cultural Adaptation Questionnaire and the Manchester Translation Reporting Questionnaire. Only two studies reported significant adaptations to the original MoCA, with one study providing detailed cultural and lexical rationale. The adaptation process assessment revealed limited reporting on the translation steps involved, with few studies detailing the original author's involvement, professional translation, pilot testing, or healthcare professional input. This systematic review suggests the use of suitable cutoff scores, educational level-based scoring adjustments, and cultural awareness are key for adequate screening using the MoCA in LAC Spanish-speaking countries. PROSPERO Study registration number: CRD42023465794.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".