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Updated: Jan 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Psychometric Properties of the UCSF Fein MAC Educational & Developmental History Questionnaire:: A Novel Screening
Ezra Mauer1,2, Isabel E Allen3, Rian Bogley1,2
1Dyslexia Center, Departments of Neurology and Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco.
Background:
Increasing evidence suggests that neurodevelopmental differences substantially alter the expression and course of later-life neurodegenerative diseases. Standard approaches for determining early-life neurodevelopmental differences in aging populations rely largely on chart-based reviews, which poses a methodological challenge due to the varied quality and completeness of medical records. To overcome this limitation, we created the novel Educational & Developmental History (EDevHx) form, a retrospective questionnaire designed to capture early developmental features. Here, we evaluated its psychometric properties among a large sample of cognitively unimpaired, aging adults.
Methods:
The EDevHx was completed by 677 clinically normal adults aged 46-95 years who underwent standard evaluations to establish their cognitively healthy status.
Results:
EDevHx items grouped into hypothesized domains (Language, Motor, Visuospatial/Mathematical, Attention, Social) significantly loaded onto their associated domains via confirmatory factor analysis. For each factor, the associated items significantly related to the factor while holding other items constant, indicating a lack of redundancy. Multidimensional scaling analysis showed items were visually grouped within hypothesized domains. Each factor demonstrated acceptable internal consistency. Test-retest reliability ranged from moderate to good, except for the Social factor's, which was poor. Each factor (as well as two items theorized not to map onto any hypothesized domain) demonstrated convergent/divergent validity with validated questionnaires/neurocognitive tests.
Conclusion:
The EDevHx tool represents an easily scalable and robust method for capturing early developmental features among aging populations. The present study demonstrates its strong psychometric properties, supporting its immediate and widespread integration into clinical and research practices alike.
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