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Updated: Sep 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Neuropsychological Approach to Mild Cognitive Impairment
Dennis P Alfano1, Julia A Grummisch1, Jennifer L Gordon1
1Department of Psychology, University of Regina, Regina, SK, Canada.
Objective:
A neuropsychological approach to the detection and classification of mild cognitive impairment (MCI) using "gold standard" clinical ratings (CRs) was examined in a sample of independently functioning community dwelling seniors. The relationship between CRs and life satisfaction, concurrent validity of cognitive screening measures, and agreement between CRs and existing criteria for MCI were also determined.
Method:
One hundred and forty-two participants, aged 75 years and older, were administered a comprehensive battery of neuropsychological tests, along with self-report measures of psychological and psychosocial functioning, and functional independence. CRs were based on demographically corrected neuropsychological variables.
Results:
The prevalence of MCI identified using CRs in this sample was 26.1%. Single and multiple domain subtypes of MCI were readily identified with subtypes reflecting Amnestic and Executive Function impairment predominating. Executive Function was a significant predictor of Life Satisfaction. The MoCA and MMSE both showed weak performance in detecting MCI based on CRs. There was substantial agreement between CRs and the classification criteria for MCI defined by Petersen/Winblad and Jak/Bondi. A global deficit score had near perfect performance as a proxy for CRs in detecting MCI in this sample.
Conclusions:
The results provide strong support for the utility of neuropsychological CRs as a "gold standard" operational definition in the detection and classification of MCI in older adults.
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