A prospectively data-base was setting up, collecting CoMDA and in-depht-neuropsychologocal-battery scores, obtained from the evaluation of 500 patients with parkinsonisms. Data were analyzed to compare the classification of patient cognition profile, obtained with CoMDA, MMSE, MoC and FAB, with that obtained from in-depth neuropsychological evaluation. A very high percentage of false negative emerged, for MMSE, MoCA and FAB. Conversely, the CoMDA score significantly reduces the rate of false negative.~This new tool, namely CoMDA (Cognition in Movement Disorders Assessment), was composed, by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Moreover, we created a machine learning, namely Neural Net 91classification able to classify the cognitive profile and predict patients' at risk of dementia, providing a prediction of the findings resulting from a in-depht neuropsychological evaluation.~CoMDA and the related Neural Net 91classification represent a reliable, time-sparing screening instrument, which is much more powerful of other common, widely-adopted tools.
Based on a prospectively collected data analysis, a new tool, namely CoMDA (Cognition in Movement Disorders Assessment) is developed by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). A machine learning, able to classify the cognitive profile and predict patients' at risk of dementia, is created.