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Updated: May 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Using the Mini-Mental State Examination to screen for delirium in elderly patients with hip fracture
G I Ringdal1, K Ringdal, V Juliebø
1Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway. gerd.inger.ringdal@svt.ntnu.no
Background:
The main aim of this paper was to examine the usefulness of the Mini-Mental State Examination (MMSE) for screening delirium in elderly patients with hip fracture.
Methods:
The sample included 364 elderly patients with hip fracture admitted to two hospitals in Oslo. Delirium was assessed by the Confusion Assessment Method (CAM) as an approximation of the gold standard, DSM-IV. To evaluate the psychometric properties of the MMSE scale, we used the Mokken nonparametric latent trait model for unidimensional scaling.
Results:
In total, 76 (21%) patients were diagnosed with delirium based on the CAM, and 141 (43%) had preexisting cognitive impairment. As a screening tool, the recommended MMSE cut-point of 24 showed an acceptable sensitivity of 88% with a specificity of 54%. The prediction of delirium, based on logistic regression on the MMSE total score and on the 5 items selected by the stepwise logistic regression procedure, gave clearly less acceptable results.
Conclusions:
Our study indicates that the MMSE may be useful in screening for delirium, but the high percentage of false positives shows that it does not have diagnostic potential in patients with hip fracture.
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