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Updated: Mar 14, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Agreement and conversion formula between mini-mental state examination and montreal cognitive assessment in an
Luqman Helmi1, David Meagher1, Edmond O'Mahony1
1Luqman Helmi, Geraldine McCarthy, Dimitrios Adamis, Sligo Medical Academy, NUI Galway and Sligo/Leitrim Mental Health Services, F91 CD34 Sligo, Ireland.
Aim:
To explore the agreement between the mini-mental state examination (MMSE) and montreal cognitive assessment (MoCA) within community dwelling older patients attending an old age psychiatry service and to derive and test a conversion formula between the two scales.
Methods:
Prospective study of consecutive patients attending outpatient services. Both tests were administered by the same researcher on the same day in random order.
Results:
The total sample (n = 135) was randomly divided into two groups. One to derive a conversion rule (n = 70), and a second (n = 65) in which this rule was tested. The agreement (Pearson's r) of MMSE and MoCA was 0.86 (P < 0.001), and Lin's concordance correlation coefficient (CCC) was 0.57 (95%CI: 0.45-0.66). In the second sample MoCA scores were converted to MMSE scores according to a conversion rule from the first sample which achieved agreement with the original MMSE scores of 0.89 (Pearson's r, P < 0.001) and CCC of 0.88 (95%CI: 0.82-0.92).
Conclusion:
Although the two scales overlap considerably, the agreement is modest. The conversion rule derived herein demonstrated promising accuracy and warrants further testing in other populations.
