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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Communicating mild cognitive impairment diagnoses with and without amyloid imaging
Joshua D Grill1,2,3, Liana G Apostolova4,5, Szofia Bullain6,7
1Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA. jgrill@uci.edu.
Alzheimer'S Research & Therapy
|May 6, 2017
Summary
Clinicians should discuss mild cognitive impairment (MCI) diagnoses carefully, using amyloid imaging as a tool to inform prognosis and etiology. Proper patient education and expectation management are crucial for effective communication and decision-making.
Area of Science:
- Neurology
- Geriatrics
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) presents diagnostic and prognostic uncertainties for clinicians.
- Communicating MCI to patients and families can be challenging.
- Amyloid imaging offers potential insights but introduces new labeling complexities.
Purpose of the Study:
- To provide recommendations for clinicians managing patients with MCI.
- To guide the use of amyloid imaging in MCI assessment.
- To improve communication strategies for MCI diagnosis and prognosis.
Main Methods:
- Convened a workgroup of experts.
- Formulated clinical recommendations for MCI patient care.
- Reviewed literature on amyloid imaging in MCI.
Main Results:
- MCI diagnosis should validate patient concerns and clarify that impairment is abnormal.
- Amyloid imaging can aid in determining MCI etiology and prognosis for eligible patients.
- Clear communication of amyloid imaging limitations, results, and implications is essential.
Conclusions:
- Effective patient education and shared decision-making are vital for MCI management.
- Amyloid imaging can support clinicians but requires careful expectation management.
- Clear communication of scan results is critical for appropriate use of amyloid imaging information.
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