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Updated: Jun 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Minimizing misdiagnosis: psychometric criteria for possible or probable memory impairment
Brian L Brooks1, Grant L Iverson, Howard H Feldman
1Alberta Health Services and University of Calgary, Alta., Canada. brian.brooks@albertahealthservices.ca
Background/Aims:
Memory impairment can be easily misdiagnosed in older adults because obtaining some low scores is common. The objective of the present study is to present new psychometric criteria for determining 'possible' and 'probable' memory impairment.
Methods:
We propose criteria based on an analysis of performance from 450 healthy older adults (55-87 years old) on 3 measures from the WMS-III: Logical Memory, Word List, and Visual Reproduction. These measures yield 8 age-adjusted scores for learning, recall, and recognition. The proposed criteria for memory impairment are based on the prevalence of low scores when simultaneously examining all 8 scores and are stratified by current intelligence, estimated premorbid intelligence, and education. The criteria are subsequently validated on 100 healthy older adults and 34 patients with 'possible' or 'probable' Alzheimer's Disease (AD).
Results:
Tables with cutoffs and false-positive rates are presented for clinical use. In the validation cohort there were no misclassifications in AD patients.
Conclusion:
This study presents steps in the development of proposed psychometric criteria that, in conjunction with clinical judgment, could minimize the misdiagnosis of memory impairment. It is important to reduce misdiagnosis in order to (a) optimize patient care, (b) provide an accurate foundation for identifying biological and neurological markers, and (c) successfully develop disease-modifying treatments. Further validation in a sample of older adults with lesser degrees of cognitive impairment is needed.
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