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Updated: Jun 3, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Spanish translation and validation of an Executive Battery 25 (EB25) and its shortened version (ABE12) for executive
1Facultad de Psicología, Universidad Nacional de Rosario, Rosario, Argentina.
Introduction:
There is a need for clinically administered instruments capable of detecting executive dysfunction in dementia.
Objective:
The translation and validation of Executive Battery 25 (EB25) and a short version for screening of executive dysfunction in dementia.
Methods:
The original battery was translated and validated using convergent and divergent correlation in 66 mild dementia patients (CDR 1) matched with 66 controls. EB25 consists of 25 items which detect executive dysfunction. Convergent correlation was made with 7 tests assessing executive dysfunction, the Frontal Systems Behaviour Scale (FrSBe) and Disability Fast Assessment Scale.
Results:
Patients had higher scores than controls and correlated with the Stroop Test, verbal fluency test and Frontal Behaviour Inventory. Only 12 out of 25 items were needed to separate both groups, which were used to build an abbreviated Executive Battery with equal psychometric properties and discriminative power. The cut-off point for EB25 was 12, and 7 for the abbreviated version. A cut-off point of 12 was able to discriminate between ¿Alzheimer's disease? (AD) and frontotemporal lobe dementia (FTLD).
Conclusions:
EB25 and AEB12 enable executive dysfunction to be detected in mild dementia. On the other hand, AEB12 exhibits better psychometric properties than the original battery, allowing discrimination between AD and FTLD and is completed in less time.
