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Updated: Aug 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Retention weighted recall improves discrimination of Alzheimer's disease
Herman Buschke1, Martin J Sliwinski, Gail Kuslansky
1Department of Neurology, Kennedy 912, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA. buschke@aecom.yu.edu
Abstract:
Impaired recall for early items (primacy) and late items (recency) on word list recall tests are seen in Alzheimer's disease (AD). We compared conventional scoring on the Telephone Instrument for Cognitive Status (TICS) recall list with scorings based on retention-weighted recall (RWR: each item weighted by its serial position) in older adults participating in a community-based aging study. Subjects with mild AD (N=18) did not differ from those without dementia (N=231) with respect to recency (46% vs. 59%, p = 0.2), but had impaired primacy (2% vs. 39%, p < .001) on word recall on the TICS. RWR scoring improved the effect size (1.52 SD) compared to conventional scoring (1.08 SD). With a fixed sensitivity of 85%, specificity was lower using conventional scoring (56%) than RWR (76%) scoring. Our findings suggest that optimized RWR scoring of word list free recall can improve detection of mild AD compared to conventional scoring.
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