Rendimiento diagnóstico del Mini-Mental State Examination y tres pruebas de cribado transcultural en poblaciones mayoritarias y diversas
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Updated: Jan 8, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Alfonso Delgado-Álvarez1,2, Cristina Delgado-Alonso1, María Valles-Salgado1
1Department of Neurology, Hospital Clinico San Carlos, San Carlos Institute for Health Research (IdiSSC), Universidad Complutense, Madrid, Spain.
Abstract:
The selection of the optimal cognitive screening test is highly relevant in the diagnosis of Alzheimer's disease (AD). We aimed to compare the diagnostic classification properties of the Mini-Mental State Examination (MMSE) against three cross-cultural tests (Cross-Cultural Dementia screening (CCD), Multicultural Cognitive Examination (MCE), Rowland Universal Dementia Assessment Scale). In total, 241 participants were recruited from majority and diverse populations: 155 healthy controls and 86 with AD. MMSE, MCE, and CCD were the best predictors in the majority sample, whereas MCE and CCD performed better in the diverse sample. The good diagnostic properties of MCE in majority and diverse populations suggest that it may be a valuable option, considering the increasing diversity of populations.
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