ArticleJournal of movement disorders2024
Comparing Montreal Cognitive Assessment Performance in Parkinson's Disease Patients: Age- and Education-Adjusted Cutoffs vs. Machine Learning.
Article in Journal of movement disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- Does cognition affect supervised and unsupervised mobility differently in people with Parkinson's disease? A cross-sectional study.Journal of neuroengineering and rehabilitation · 2026Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveThe Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson's disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels.
methodsIn this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning.
methodsand different cutoff criteria were conducted on an additional 91 consecutive patients with PD.
resultsThe cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60-80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10-12 years, and 21 or 20 years for 7-9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250).
conclusionBoth the age- and education-adjusted cutoff.
methodsand machine learning.
methodsdemonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.
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Registered trials
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