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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Defining and Measuring Cognitive Decline and Clinical Relevance
Carolin Kurz1, Paul Newhouse2, Kate A Wyman-Chick3
1Department of Psychiatry and Psychotherapy, Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital, Munich, Germany. carolin.kurz@mri.tum.de.
Abstract:
Current frameworks for cognitive impairment increasingly incorporate biomarker information, yet clinical classification remains grounded in the demonstration of objective cognitive decline and its functional consequences. This paper examines the balance between biological and clinical definitions and discusses implications for diagnosis, care, and research. Mild cognitive impairment (MCI) or mild neurocognitive disorder represents a clinically meaningful intermediate stage between normal aging and dementia, enabling early detection, timely intervention, and longitudinal monitoring. In routine care, comprehensive neuropsychological testing for all patients is not feasible; instead, a tiered diagnostic approach combining brief screening, clinical assessment, informant information, and targeted referral appears most practical and diagnostically robust. In research settings, outcomes that integrate cognitive performance with functional measures better capture clinically meaningful change than cognition-only endpoints. However, heterogeneity in definitions and outcome measures limits comparability across studies, and challenges remain regarding feasibility, cultural validity, and the integration of patient-relevant outcomes. Clinically useful definitions of cognitive impairment should integrate objective deficits with real-world impact. Pragmatic staged diagnostic pathways are best suited for routine care, while future work should focus on standardization, feasibility, and the alignment of research outcomes with patient-relevant benefits.
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