Article

Comprehensive Demographic Correction Improves Sensitivity and Reduces Bias in Cognitive Assessment

2026-06-30

Abstract excerpt

<h4>Background</h4> Scores on neuropsychological assessments are typically corrected for the influences of age, education, and gender (AEG). However, other demographic factors, such as crystallized ability and race/ethnicity, independently affect test performance. As a result, standard scores systematically over- or under-classify impairment in patients whose demographic profile differs from that of the reference...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 232c6412-67a1-5ccc-a33d-05725f6c6b54
DOI: 10.64898/2026.06.27.26356750

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Comprehensive Demographic Correction Improves Sensitivity and Reduces Bias in Cognitive AssessmentDOI 10.64898/2026.06.27.26356750Read

Dementia Risk Scores,<i>APOE,</i>and risk of Alzheimer disease: one size does not fit allSemantic proximity Read

Standardized Comparison of Clinical, Cognitive, Genetic, Neuroimaging, and Fluid Biomarkers for Predicting 24-Month Progression from Mild Cognitive Impairment to Alzheimer’s DiseaseSemantic proximity Read

Predicting 24-Month MCI-to-Alzheimer’s Conversion Using Routine Clinical Assessments Without Neuroimaging or Genetic TestingSemantic proximity Read

Assessing Cognitive Decline and Dementia Risk in Black and White Older Adults with Blood Biomarkers pTau217, GFAP, NfL, and Aβ ratioSemantic proximity Read

Machine Learning-Based Multiclass Classification of Cognitive Stages Using Plasma Biomarkers, Clinical Assessments, and Genetic Features: A Repeated Nested Cross-Validation Study in ADNI with External Validation in CNTNSemantic proximity Read

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain DataSemantic proximity Read

Algorithmic Fairness of Machine Learning Models for Alzheimer’s Disease Progression PredictionSemantic proximity Read

Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study.Semantic proximity Read

Diagnostic Accuracy of Cognitive Screening Tools Validated for Older Adults in Iran: A Systematic Review and Meta-analysisSemantic proximity Read

Machine learning models for dementia risk prediction: Evidence from the Sydney Memory and Ageing StudySemantic proximity Read

A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological MeasuresSemantic proximity Read

The effects of COVID-19 on cognitive performance in a community-based cohort: A COVID Symptom Study Biobank observational studySemantic proximity Read