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Evidence-Based Complementary and Alternative Medicine - Volume 2014 2014, Article ID 694804, 11 pages -

Review Article

Military Medical Research, Samueli Institute, 2101 East Coast Highway, Suite 300, Corona Del Mar, CA 92625, USA

Department of Planning, Policy, and Design, School of Social Ecology, University of California Irvine, Irvine, CA 92697-7075, USA

Samueli Institute, 1737 King Street, Alexandria, VA 22314, USA

Received 22 September 2013; Revised 14 November 2013; Accepted 17 November 2013; Published 6 March 2014

Academic Editor: Cheryl Hawk

Copyright © 2014 Raheleh Khorsan and Cindy Crawford. This is an open access article distributed under the creative commons attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Evidence rankings do not consider equally internal IV, external EV, and model validity MV for clinical studies including complementary and alternative medicine-integrative health care CAM-IHC research. This paper describe this model and offers an EV assessment tool EVAT© for weighing studies according to EV and MV in addition to IV. Methods. An abbreviated systematic review methodology was employed to search, assemble, and evaluate the literature that has been published on EV-MV criteria. Standard databases were searched for keywords relating to EV, MV, and bias-scoring from inception to Jan 2013. Tools identified and concepts described were pooled to assemble a robust tool for evaluating these quality criteria. Results. This study assembled a streamlined, objective tool to incorporate for the evaluation of quality of EV-MV research that is more sensitive to CAM-IHC research. Conclusion. Improved reporting on EV can help produce and provide information that will help guide policy makers, public health researchers, and other scientists in their selection, development, and improvement in their research-tested intervention. Overall, clinical studies with high EV have the potential to provide the most useful information about “real-world” consequences of health interventions. It is hoped that this novel tool which considers IV, EV, and MV on equal footing will better guide clinical decision making.

Autor: Raheleh Khorsan and Cindy Crawford

Fuente: https://www.hindawi.com/


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