Applicability of the MoCA‐S test in populations with little education in Colombia
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to report on the use of the Spanish version of the Montreal Cognitive Assessment (MoCA-S) as cognitive screening tool in a population aged 65 to 74 years in the Andes Mountains of Colombia, assessing the influence of education, and to examine its test-retest reliability. METHODS: We performed a cross-sectional study of 150 subjects aged 65 to 74 years recruited from older community social centers in Manizales, Colombia. The Leganes Cognitive Test (LCT), a cognitive screening test for populations with low education, was used to exclude those who were likely to have dementia. The associations between the MoCA total score and cognitive domains and education were examined in the total sample and in those likely free of dementia. MoCA-S test-retest reliability was estimated by the intraclass correlation coefficient (ICC) between two measurements taken 7 days apart. RESULTS: Participants had low levels of formal education (mean years of schooling, 4.8). According to the LCT, the proportion of people screening positive for dementia was 16% (n = 24). The mean MoCA-S scores were 16.1/30 among illiterate subjects, 18.2/30 among those with incomplete primary school, and 20.3/30 among those with complete primary school (p < 0.001). Errors were frequent in the cube and clock drawing, attention-serial subtraction, verbal fluency, and abstraction. Test-retest reliability was high, ICC = 0.86, 95% CI (0.76-0.93). CONCLUSION: The MoCA-S has high reliability in low-educated older Colombians, but scores were strongly dependent on years of education. Social and cultural factors must be considered when interpreting MoCA-S given the high error rates on items that depend on the ability to read and write and on culture.
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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 | 0.001 | 0.006 |
| 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".