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Updated: Feb 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive screening test in primary care: cut points for low education
Juliana Emy Yokomizo1, Katrin Seeher2, Glaucia Martins de Oliveira3
1Universidade de São Paulo. Faculdade de Medicina. Programa Terceira Idade (PROTER), Instituto de Psiquiatria. São Paulo, SP, Brasil.
Objective:
To establish the diagnostic accuracy of the Brazilian version of the General Practitioner Assessment of Cognition (GPCOG-Br) compared to the Mini-Mental State Examination (MMSE) in individuals with low educational level.
Methods:
Ninety-three patients (≥ 60 years old) from Brazilian primary care units provided sociodemographic, cognitive, and functional data. Receiver operating characteristics, areas under the curve (AUC) and logistic regressions were conducted.
Results:
Sixty-eight patients with 0-4 years of education. Cases (n = 44) were older (p = 0.006) and performed worse than controls (n = 49) on all cognitive or functional measures (p < 0.001). The GPCOG-Br demonstrated similar diagnostic accuracy to the MMSE (AUC = 0.90 and 0.91, respectively) and similar positive and negative predictive values (PPV/NPV, respectively: 0.79/0.86 for GPCOG-Br and 0.79/0.81 for MMSE). Adjusted cut-points displayed high sensitivity (all 86%) and satisfactory specificity (65%-80%). Lower educational level predicted lower cognitive performance.
Conclusions:
The GPCOG-Br is clinically well-suited for use in primary care.
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