Literature DB >> 29314554

Going from evidence to recommendations: Can GRADE get us there?

Mathew Mercuri1,2,3, Brian Baigrie2, Ross E G Upshur4.   

Abstract

The evidence based medicine movement has championed the need for objective and transparent methods of clinical guideline development. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) framework was developed for that purpose. Central to this framework is criteria for assessing the quality of evidence from clinical studies and the impact that body of evidence should have on our confidence in the clinical effectiveness of a therapy under examination. Grades of Recommendation, Assessment, Development, and Evaluation has been adopted by a number of professional medical societies and organizations as a means for orienting the development of clinical guidelines. As a result, the method of GRADE has implications on how health care is delivered and patient outcomes. In this paper, we reveal several issues with the underlying logic of GRADE that warrant further discussion. First, the definitions of the "grades of evidence" provided by GRADE, while explicit, are functionally vague. Second, the "criteria for assigning grade of evidence" is seemingly arbitrary and arguably logically incoherent. Finally, the GRADE method is unclear on how to integrate evidence grades with other important factors, such as patient preferences, and trade-offs between costs, benefits, and harms when proposing a clinical practice recommendation. Much of the GRADE method requires judgement on the part of the user, making it unclear as to how the framework reduces bias in recommendations or makes them more transparent-both goals of the programme. It is our view that the issues presented in this paper undermine GRADE's justificatory scheme, thereby limiting the usefulness of GRADE as a tool for developing clinical recommendations.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  GRADE; clinical recommendations; evidence-based medicine; practice guidelines

Mesh:

Year:  2018        PMID: 29314554     DOI: 10.1111/jep.12857

Source DB:  PubMed          Journal:  J Eval Clin Pract        ISSN: 1356-1294            Impact factor:   2.431


  5 in total

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Authors:  George D Chloros; Apostolos D Prodromidis; Peter V Giannoudis
Journal:  Injury       Date:  2022-04-20       Impact factor: 2.687

2.  High quality (certainty) evidence changes less often than low-quality evidence, but the magnitude of effect size does not systematically differ between studies with low versus high-quality evidence.

Authors:  Benjamin Djulbegovic; Muhammad Muneeb Ahmed; Iztok Hozo; Despina Koletsi; Lars Hemkens; Amy Price; Rachel Riera; Paulo Nadanovsky; Ana Paula Pires Dos Santos; Daniela Melo; Ranjan Pathak; Rafael Leite Pacheco; Luis Eduardo Fontes; Enderson Miranda; David Nunan
Journal:  J Eval Clin Pract       Date:  2022-01-28       Impact factor: 2.336

Review 3.  The Effectiveness of High-Frequency Repetitive Transcranial Magnetic Stimulation on Patients with Neuropathic Orofacial Pain: A Systematic Review of Randomized Controlled Trials.

Authors:  Yingxiu Diao; Yuhua Xie; Jiaxin Pan; Manxia Liao; Hao Liu; Linrong Liao
Journal:  Neural Plast       Date:  2022-08-24       Impact factor: 3.144

4.  Clinical effectiveness of nimodipine for the prevention of poor outcome after aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis.

Authors:  Guangzhi Hao; Guangxin Chu; Pengyu Pan; Yuwei Han; Yunzheng Ai; Zuolin Shi; Guobiao Liang
Journal:  Front Neurol       Date:  2022-09-21       Impact factor: 4.086

5.  EA3: A softmax algorithm for evidence appraisal aggregation.

Authors:  Francesco De Pretis; Jürgen Landes
Journal:  PLoS One       Date:  2021-06-17       Impact factor: 3.240

  5 in total

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