Urogenit Tract Infect.  2019 Aug;14(2):64-70. 10.14777/uti.2019.14.2.64.

Korean Translation of the GRADE Series Published in the BMJ, ‘GRADE: What Is “Quality of Evidence” and Why Is It Important to Clinicians?’ (A Secondary Publication)

Affiliations
  • 1Department of Urology, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.
  • 2Department of Urology, Yonsei University Wonju College of Medicine, Wonju, Korea.
  • 3Institute of Evidence Based Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
  • 4Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea.
  • 5Department of Urology, Pusan National University Hospital, Busan, Korea.
  • 6Department of Urology, Catholic University of Daegu School of Medicine, Daegu, Korea.
  • 7Department of Urology, College of Medicine, Konyang University, Daejeon, Korea.
  • 8Department of Urology, Chonnam National University Medical School, Hwasun, Korea. urohwang@gmail.com

Abstract

This article is second translation of a GRADE series published in the BMJ to create a highly structured, transparent, and informative system for rating quality of evidence for developing recommendations. The process to develop a guideline, we should formulate a clear question with specification of all outcomes of importance to patients. Grading of Recommendations, Assessment, Development and Evaluation (GRADE) offers four levels of evidence quality: high, moderate, low, and very low for these patient-important outcomes. Randomized trials begin as high quality evidence and observational studies as low quality evidence. Although randomized trials begin as high quality evidence, quality may be downgraded as a result of study limitations (risk of bias), inconsistency (variability in results), indirectness, imprecision (wide confidence intervals), or publication bias. While the quality of evidence derived from observational studies starts at "˜low' but may be upgraded based on a very large magnitude of effect, a dose-response gradient, and if all plausible biases would reduce an apparent treatment effect.

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