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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Montreal Cognitive Assessment for evaluating the cognitive impairment in patients with schizophrenia: A systematic

Elena Cecilia Rosca¹, Amalia Cornea¹, Mihaela Simu¹

¹University of Medicine and Pharmacy "Victor Babes" Timisoara, Department of Neurology, Romania; Clinical Emergency County Hospital Timisoara, Department of Neurology, Romania.

General Hospital Psychiatry

|June 6, 2020

Abstract

Objective:

This study aims to systematically review the evidence on the accuracy of the Montreal Cognitive Assessment (MoCA) test for evaluating the presence of cognitive impairment in patients with schizophrenia and to outline the quality and quantity of research evidence available about the accuracy of MoCA in this population.

Methods:

We conducted a systematic literature review, searching four databases from inception until April 2020.

Results:

We identified only three cross-sectional studies, two case - control studies, three studies comparing MoCA with Mini-Mental State Examination (MMSE) and four prevalence studies that met the inclusion criteria. Publication period ranged from 2012 to 2020.

Conclusions:

In patients with schizophrenia, the MoCA test provides information about general cognitive functioning disturbances. A lower threshold than the original cut-off of 26 is probably more useful for optimal screening, as it lowers false positive rates and improves diagnostic accuracy. Nonetheless, more studies are necessary in this direction.

Keywords:

Cognition Montreal Cognitive Assessment Schizophrenia Screening Systematic review

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