• Ignacio Neumann, Nancy Santesso, Elie A Akl, David M Rind, Per Olav Vandvik, Pablo Alonso-Coello, Thomas Agoritsas, Reem A Mustafa, Paul Elias Alexander, Holger Schünemann, and Gordon H Guyatt.
• Department of Clinical Epidemiology and Biostatistics, McMaster University, Room 2C12, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Department of Internal Medicine, School of Medicine, Pontificia Universidad Catolica de Chile, Alameda 340, Santiago, Chile. Electronic address: ignacio.neumann@gmail.com.
• J Clin Epidemiol. 2016 Apr 1; 72: 45-55.

AbstractAn increasing number of organizations worldwide are using new and improved standards for developing trustworthy clinical guidelines. One of such approaches, developed by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group, offers systematic and transparent guidance in moving from evidence to recommendations. The GRADE strategy concentrates on four factors: the balance between benefits and harms, the certainty of the evidence, values and preferences, and resource considerations. However, it also considers issues around feasibility, equity, and acceptability of recommendations. GRADE distinguishes two types of recommendations: strong and weak. Strong recommendations reflect a clear preference for one alternative and should apply to all or almost all patients, obviating the need for a careful review of the evidence with each patient. Weak recommendations are appropriate when there is a close balance between desirable and undesirable consequences of alternative management strategies, uncertainty regarding the effects of the alternatives, uncertainty or variability in patients' values and preferences, or questionable cost-effectiveness. Weak recommendations usually require accessing the underlying evidence and a shared decision-making approach. Clinicians using GRADE recommendations should understand the meaning of the strength of the recommendation, be able to critically appraise the recommendation, and apply trustworthy recommendations according to their strength. Copyright © 2016 Elsevier Inc. All rights reserved.

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