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Updated: Aug 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Diagnostic efficiency of the Trail Making Test as a function of cut-off score, diagnosis, and age
Abstract:
This study is a reinvestigation of the effectiveness of the Trail Making Test in discriminating between brain-damaged (n = 51) and pseudoneurologic (n = 72) subjects. All subjects were hospitalized male veterans at the Allen Park Veterans Administration Hospital. An analysis of covariance showed that the pseudoneurologic subjects performed at a significantly higher level than the brain-damaged subjects. Further analysis with two different cut-off scores exhibited unequal discriminatory power throughout the whole range of Trail Making Test scores. These results suggested diagnosing only when the scores were 9 or lower and 13 or higher. This use of extreme scores resulted in an over-all hit rate of 87% in the study. Comparisons with five major subgroupings of the pseudoneurologic subjects yielded only one significant difference: more accurate discrimination with the 12 cut-off score between 10 general-medical and peripheral nerve-damaged subjects (100% correct) and 18 psychotic subjects (39% accuracy).

