Accuracy of the Mini-Mental State Examination and Montreal Cognitive Assessment in Detecting Cognitive Impairment in Older Adults: A Comparative Study Adjusted for Educational Level
Bibliographic record
Abstract
Early detection of cognitive decline in older adults is essential for implementing timely interventions. This study aimed to compare the accuracy of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in identifying cognitive impairment in community-dwelling older adults, while considering the effect of educational level. A cross-sectional, analytical study was conducted with 101 individuals aged 60 years or older, classified into cognitively intact and cognitively impaired non-demented (CIND) groups. Cognitive performance was assessed using the MMSE and MoCA, and the results were adjusted for years of schooling. Diagnostic accuracy was analyzed using Receiver Operating Characteristic (ROC) curves. The MoCA showed superior performance compared to the MMSE, with a significantly larger area under the ROC curve (AUC = 0.861 vs. 0.782; p = 0.037), particularly among participants with ≤ 8 years of education. These findings suggest that the MoCA is more sensitive in detecting early cognitive impairment and should be considered a preferable screening tool in clinical and research settings, especially in populations with heterogeneous educational backgrounds.
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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 | 0.007 | 0.018 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".