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证据质量分级与推荐强度

Grading quality of evidence and strength of recommendations.

作者信息

Atkins David, Best Dana, Briss Peter A, Eccles Martin, Falck-Ytter Yngve, Flottorp Signe, Guyatt Gordon H, Harbour Robin T, Haugh Margaret C, Henry David, Hill Suzanne, Jaeschke Roman, Leng Gillian, Liberati Alessandro, Magrini Nicola, Mason James, Middleton Philippa, Mrukowicz Jacek, O'Connell Dianne, Oxman Andrew D, Phillips Bob, Schünemann Holger J, Edejer Tessa Tan-Torres, Varonen Helena, Vist Gunn E, Williams John W, Zaza Stephanie

出版信息

BMJ. 2004 Jun 19;328(7454):1490. doi: 10.1136/bmj.328.7454.1490.

Abstract

Users of clinical practice guidelines and other recommendations need to know how much confidence they can place in the recommendations. Systematic and explicit methods of making judgments can reduce errors and improve communication. We have developed a system for grading the quality of evidence and the strength of recommendations that can be applied across a wide range of interventions and contexts. In this article we present a summary of our approach from the perspective of a guideline user. Judgments about the strength of a recommendation require consideration of the balance between benefits and harms, the quality of the evidence, translation of the evidence into specific circumstances, and the certainty of the baseline risk. It is also important to consider costs (resource utilisation) before making a recommendation. Inconsistencies among systems for grading the quality of evidence and the strength of recommendations reduce their potential to facilitate critical appraisal and improve communication of these judgments. Our system for guiding these complex judgments balances the need for simplicity with the need for full and transparent consideration of all important issues.

摘要

临床实践指南及其他建议的使用者需要知道他们对这些建议能有多大的信心。系统且明确的判断方法可以减少错误并改善沟通。我们开发了一种用于对证据质量和建议强度进行分级的系统,该系统可应用于广泛的干预措施和背景情况。在本文中,我们从指南使用者的角度对我们的方法进行了总结。对建议强度的判断需要考虑利弊平衡、证据质量、将证据转化为具体情况以及基线风险的确定性。在提出建议之前考虑成本(资源利用)也很重要。证据质量分级系统和建议强度分级系统之间的不一致性降低了它们促进批判性评估和改善这些判断沟通的潜力。我们用于指导这些复杂判断的系统在简单性需求与全面、透明地考虑所有重要问题的需求之间取得了平衡。

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