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Updated: Mar 7, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Bridging cognitive screening tests in neurologic disorders: A crosswalk between the short Montreal Cognitive
David R Roalf1, Tyler M Moore1, Dawn Mechanic-Hamilton2
1Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Introduction:
To provide a crosswalk between the recently proposed short Montreal Cognitive Assessment (s-MoCA) and Mini-Mental State Examination (MMSE) within a clinical cohort.
Methods:
A total of 791 participants, with and without neurologic conditions, received both the MMSE and the MoCA at the same visit. s-MoCA scores were calculated and equipercentile equating was used to create a crosswalk between the s-MoCA and MMSE.
Results:
As expected, s-MoCA scores were highly correlated (Pearson r = 0.82, P < .001) with MMSE scores. s-MoCA scores correctly classified 85% of healthy older adults and 91% of individuals with neurologic conditions that impair cognition. In addition, we provide an easy to use table that enables the conversion of s-MoCA score to MMSE score.
Discussion:
The s-MoCA is quick to administer, provides high sensitivity and specificity for cognitive impairment, and now can be compared directly with the MMSE.
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