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Updated: Jan 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validity of the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L) for Mexican Subjects with
A Kammar-García1, P Peña-Gonzalez1, J Sigg-Alonso2
1Instituto Nacional de Geriatría, Ciudad de México 10200, Mexico.
Abstract:
Background/Objectives: Alzheimer's disease (AD) often begins with episodic memory deficits, detectable in Mild Cognitive Impairment (MCI). The Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L) shows promise for early detection, but lacks validation in Mexico. Methods: We assessed 355 adults ≥ 60 years, classified as cognitively healthy (CHG), MCI, or mild AD, using DSM-V criteria. Participants completed neuropsychological testing including the LASSI-L. Construct, concurrent, and predictive validity were analyzed via ANOVA, correlations with the Hopkins Verbal Learning Test (HVLT), and logistic regression models controlling for age, education, and comorbidity. Results: LASSI-L scores significantly differed between groups (p < 0.0001), with recovery from proactive interference best discriminating CHG from MCI and mild AD. Strong correlations with HVLT indices supported concurrent validity. Predictive models identified semantically cued recall and free recall (CRA2 and FRB1) as robust markers, independent of education. Conclusions: LASSI-L is a valid, accessible tool for identifying typical AD-related memory impairment in older Mexican adults, supporting earlier diagnosis in low-biomarker-access settings.
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