Educational level and its Association with the domains of the Montreal Cognitive Assessment Test
Bibliographic record
Abstract
Objective: To explore the association between educational level and the scores obtained in each of the domains of the Montreal Cognitive Assessment test.Methods: This is a secondary analysis of the SABE/2012 Bogotá survey; a cross-sectional study including 2000 subjects aged ≥60years. The MoCA test was the dependent variable and was stratified by cognitive domains, incorrect answers and scores were considered. Educational level was assessed through years of formal education. Age, sex and selected medical conditions were also included to adjust the multivariate models. Bivariate analyses, fitted logistic and linear regression models were employed for analyzing association between these variables.Results: The proportion of incorrect answers increased as schooling years decreased and as age increased. In the multivariate analysis, visuospatial and executive function were the most affected domains. Educational level displayed less influence than age on short memory-recall task (standardized beta 0.19 vs -0.24). Educational level showed a greater influence than age on no-memory tasks (the sum of all other domains; standardized beta 0.50 vs -0.29).Conclusions: It seems logical to consider that performance in most domains of the MoCA is influenced by years of education. Therefore, low scores on these tasks could lead to low total MoCA scores and thus to bias and over diagnosis of cognitive impairment in patients with lower educational levels. Memory-recall domain is not affected much by education and applying it separately could be useful in patients with low educational level in whom we suspect memory impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".