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Related Experiment Video

Updated: Jul 12, 2026

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

Comprehensive Demographic Correction Improves Sensitivity and Reduces Bias in Cognitive Assessment.

David L Woods, Kathleen Hall, Isabella Jaramillo

    Medrxiv : the Preprint Server for Health Sciences
    |July 10, 2026
    PubMed
    Summary

    The new Comprehensive (C-) model improves neuropsychological assessment by incorporating additional demographic factors beyond age, education, and gender (AEG). This enhanced model reduces racial bias and increases sensitivity for detecting cognitive decline in high-functioning individuals.

    More Related Videos

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
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    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

    Published on: February 14, 2014

    Related Experiment Videos

    Last Updated: Jul 12, 2026

    Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
    06:58

    Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

    Published on: January 24, 2020

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
    10:13

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

    Published on: February 14, 2014

    Area of Science:

    • Neuropsychology
    • Psychometrics
    • Biostatistics

    Background:

    • Standard neuropsychological assessments use age, education, and gender (AEG) corrections.
    • Demographic factors like race/ethnicity and crystallized ability also impact test performance.
    • AEG corrections can lead to systematic misclassification of cognitive impairment.

    Purpose of the Study:

    • To develop and validate a Comprehensive (C-) model scoring algorithm for neuropsychological tests.
    • To improve the accuracy of cognitive assessment by including additional demographic predictors.
    • To reduce demographic disparities in the classification of cognitive impairment.

    Main Methods:

    • Developed a C-model incorporating AEG plus vocabulary, age², race/ethnicity, SES, and medication use.
    • Utilized stability-selection LASSO to identify robust predictors in 1,914 adults assessed with the California Cognitive Assessment Battery (CCAB).
    • Validated the C-model's generalizability in two distinct cohorts using cross-sample frozen-coefficient methods.

    Main Results:

    • The C-model approximately doubled explained variance compared to the AEG model (r²=0.50 vs 0.25).
    • Racial disparities in mild cognitive impairment (MCI) classification were substantially reduced (Black-vs-White ratio from 5.6 to 1.8).
    • Sensitivity improved for high-functioning individuals, reducing MCI classification ratios in low-vs-high vocabulary quartiles (from 11.3 to 2.1).

    Conclusions:

    • The C-model offers a more accurate and equitable approach to neuropsychological scoring.
    • It significantly reduces racial bias and enhances detection of cognitive decline in diverse populations.
    • Parallel use of C- and AEG-models can increase diagnostic confidence and provide additional clinical information.