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Updated: Oct 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Normative data of the Portuguese version of the Mini-Addenbrooke's Cognitive Examination
Bruno Peixoto1,2, António Machado3, Miguel Peixoto3
1CESPU, Instituto Universitário de Ciências da Saúde, Gandra.
Introduction:
Mini-Addenbrooke's Cognitive Examination (M-ACE) is a screening neurocognitive test with diagnostic accuracy for mild cognitive impairment and dementia. The present study aims to determine the construct validity, concurrent and divergent validity and to determine the normative equation for the Portuguese version of M-ACE.
Method:
The sample is composed by 130 participants without any subjective complaint of memory loss and completely independent in daily life activities, recruited in health units, day care centers and religious and civic associations, from several districts of Portugal. The ACE-III, from which the M-ACE was extracted, and the Geriatric Depression Scale (GDS) were administered to all participants.
Results:
Reliability is considered good (α = 0.844) and the item-total correlations of the M-ACE were highly significant. M-ACE showed a positive correlation with ACE-III and a negative correlation with GDS. Schooling and age were the only variables related to M-ACE performance. The obtained model from the multiple regression was significant. A normative equation was extracted from this model.
Conclusions:
The Portuguese version of M-ACE has solid psychometric properties enabling its clinical use. The availability of M-ACE normative equation based on a healthy sample according to age and education enables the use of a brief screening tool for cognitive functioning. M-ACE does not require formal specialized training, it is a quick test which can be an advantage, for instance, in primary health care consultations.
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