A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. METHODS: We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. RESULTS: The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 (p < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall (p < 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p < 0.05). CONCLUSIONS: MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".