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
Why this work is in the frame
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Bibliographic record
Abstract
Early detection of cognitive decline in older adults is essential for implementing timely interventions. This study aimed to compare the diagnostic accuracy of the Mini-Mental State Examination (MMSE®) and the Montreal Cognitive Assessment (MoCA©) in identifying cognitive impairment among community-dwelling older adults, while considering the effect of educational level. A cross-sectional, analytical study was conducted with 90 individuals aged 60 years or older, classified into cognitively preserved and cognitively impaired groups using the Clinical Dementia Rating (CDR) scale. Cognitive performance was assessed using the MMSE and MoCA, with results analyzed using both standard and education-adjusted cut-off scores. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. The MoCA demonstrated superior discriminative ability compared to the MMSE, with a significantly larger area under the ROC curve (AUC = 0.943 vs. 0.826; p < 0.001), higher sensitivity (90.2% vs. 78.4%), and higher specificity (87.2% vs. 76.9%). When education-adjusted cut-off scores were applied, the MoCA achieved markedly improved diagnostic accuracy (87.8%) compared to the MMSE (71.1%), with stronger agreement with CDR classifications (κ = 0.746 vs. κ = −0.132). These findings demonstrate that the MoCA is more sensitive in detecting cognitive impairment and should be considered the preferred screening tool in clinical and research settings, particularly when appropriate educational adjustments are applied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it