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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Traditional Chinese medicine for mild cognitive impairment: A protocol for systematic review and network
Haiyan Wang1,2, Haiyang Yu3, Kai Song1
1College of Acupuncture and Tuina, Chengdu University of Traditional Chinese medicine, Chengdu, Sichuan Province, China.
Background:
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer disease, which is the most common form of dementia in the world. In clinical practice, traditional Chinese medicine (TCM) interventions have been administered for MCI, However, there is still uncertain about what strategy of TCM interventions treatment should be preferred in clinical practice. This study aims to evaluate the efficacy and acceptability of different TCM therapies through systematic review and network meta-analysis.
Methods:
According to the strategy, the authors will retrieve a total of 7 electronic databases by August 2020, including PubMed, the Cochrane Library, EMbase, China National Knowledge Infrastructure, China Biological Medicine, Chongqing VIP, and Wan-fang databases. After a series of screening, 2 researchers will use Aggregate Data Drug Information System and Stata software to analyze the data extracted from the randomized controlled trials of TCM therapies for MCI. The primary outcome of this study is the improvement of cognitive function and the secondary outcome is the activities of daily living, clinical efficacy, and adverse events, and the quality of the evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation instrument.
Results:
This study will provide a reliable evidence for the selection of TCM therapies in the treatment of MCI.
Conclusion:
This study will generate evidence for different TCM therapies for MCI and provide a decision-making reference for clinical research.
Ethics And Dissemination:
This study does not require ethical approval. The results will be disseminated through a peer-reviewed publication.
Osf Registration Number:
DOI 10.17605/OSF.IO/JV9KG.
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