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Updated: Aug 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Evaluation of the accuracy of two regression-based methods for estimating premorbid IQ
Bradley D Powell1, Daniel F Brossart, Cecil R Reynolds
1Department of Psychiatry and Psychology, Scott & White Clinic, 1600 University Drive, College Station, TX 77840, USA. bkpowellx3@juno.com
Abstract:
Two premorbid IQ estimation procedures were compared in a normal, non-brain-impaired sample and a clinical sample of known brain-impaired individuals. The methods used for comparison were the purely demographically based regression index (DI) developed by and the Oklahoma Premorbid Intelligence Estimate (OPIE) equation by, which uses demographic information combined with current performance tasks. The data for the normal sample were gathered from the WAIS-R standardization sample of 1880 subjects. The clinical sample was 100 patients with known cognitive impairment who had been referred to a private neuropsychology practice. The DI appeared to provide the most clinical utility as an estimate of premorbid IQ in a cognitively impaired sample. Significant differences between the two methods for specific locations of brain injury were not observed.
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