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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Identifying Cognitive Impairment in an Older Adult Using Two Different Screening Tools.

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    The Mini-Mental State Examination (MMSE) may not detect early dementia, while the Saint Louis University Mental Status (SLUMS) exam can identify cognitive impairment. This case highlights differences in screening tools for cognitive decline in older adults.

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    Area of Science:

    • Gerontology
    • Neuroscience
    • Cognitive Assessment

    Background:

    • Cognitive impairment is a growing concern in aging populations.
    • Accurate and sensitive screening tools are crucial for early detection and intervention.

    Observation:

    • A 78-year-old woman initially presented with memory concerns.
    • Serial cognitive assessments revealed discrepancies between the Mini-Mental State Examination (MMSE) and the Saint Louis University Mental Status (SLUMS) examination.

    Findings:

    • The MMSE yielded normal scores (29/30, 29/30, 26/30) across multiple visits.
    • The SLUMS examination identified potential cognitive issues, scoring 19/30 (dementia) and later 22/30 (mild neurocognitive disorder).

    Implications:

    • The study underscores the varying sensitivity of cognitive screening tools like MMSE and SLUMS.
    • SLUMS may be more effective in detecting mild cognitive impairment and early dementia compared to MMSE.
    • Cognitive assessment variability necessitates careful tool selection and interpretation in geriatric care.