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Updated: Jul 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Montreal Cognitive Assessment (MoCA): normative data for Rosario metropolitan area population, Argentina]
M Cervigni1, P Martino1, G Alfonso2
1Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET), Rosario, Argentina.
Introduction:
Population aging is a global phenomenon linked to increased life expectancy. In Argentina, it is expected that by 2025 those over 60 will represent 17.3% of the population, while by 2050 it will rise to 25.3%. Among the pathologies associated with aging, cognitive impairment and dementias represent an important problem for public health and demand effective instruments for their early detection.
Objective:
Obtain normative data for the Montreal Cognitive Assessment (MoCA) in Argentine adults and seniors in the Rosario Metropolitan Area.
Subjects And Methods:
The MoCA-Spanish version was administered according to the instructions published in the original version. An ad hoc survey was also administered to collect sociodemographic information and medical history. The influence of some sociodemographic variables on performance was analyzed. 225 adult residents of the Rosario Metropolitan Area participated in the final sample (age: M = 66.1, standard deviation = 8.7).
Results:
Educational level predicted 13% of the variance of the total MoCA score, -F (3, 221) = 12.11; p < 0.01-. Other variables considered, such as age and sex, were not significant for predicting the score.
Conclusion:
The normative data obtained suggest a cut-off point of 18 for people with primary education and of 22 for people with secondary or higher education. It should be noted that they are below those indicated in the pre-existing regulatory data. The importance of using norms adjusted to the sociocultural context is highlighted.
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