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Updated: May 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mini-Cog and Mini-Mental State Examination: agreement in a cross-sectional study with an elderly sample
Diogo Costa1, Milton Severo, Sílvia Fraga
1Institute of Public Health of the University of Porto, Department of Clinical Epidemiology, Predictive Medicine and Public Health, University of Porto Medical School, Rua das Taipas 135–139, Porto, Portugal. dmcosta@med.up.pt
Background/Aims:
We aimed to compare the Mini-Mental State Examination (MMSE) with the Mini-Cog, measuring agreement in participants' classification, using a general population sample.
Methods:
Cross-sectional evaluation of 609 community dwellers aged ≥60 years was performed by trained interviewers. Cohen's kappa and 95% confidence intervals (CI) were calculated to assess overall agreement, and Cronbach alphas computed to assess reliability. Two-parameter Item Response Theory models (difficulty and discrimination parameters) were used to assess discrimination.
Results:
Considering MMSE cut-point for scores <24, 3.1% of the participants would be 'cognitive impaired' and 6.2% considering cut-point scores <25. Following Mini-Cog's cut-point score <3, 11.3% would be impaired. For MMSE cut-point <24 and Mini-Cog <3, we observed a Cohen's kappa of 0.116 (95% CI: -0.073 to 0.305), and of 0.258 (95% CI: 0.101-0.415) for cut-point <25. The highest kappa was obtained for cut-point <26 on the MMSE and Mini-Cog <3 (kappa = 0.413). MMSE Cronbach alpha was 0.6108 and Mini-Cog's alpha was 0.2776. Co-calibration according to inherent ability is graphically presented.
Conclusions:
Agreement between scales seems fragile in our sample. The discriminative and reliability analysis suggests a better performance for subsets of the MMSE compared with the Mini-Cog. Usefulness of calibrated scores is discussed.
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