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定义对健康益处和危害的判断的决策门,使用推评估,开发和评估 (GRADE) 证据对决策 (EtD) 框架的分级:随机方法研究 (GRADE-THRESHOLD)
Gian Paolo Morgano1, Wojtek Wiercioch2, Daniele Piovani3
1European Commission, Joint Research Centre (JRC), Ispra, Italy; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
这项研究推导出经验决策值 (DTs),以客观地评估证据对决策 (EtD) 框架中的健康结果大小. 这些数据技术提高了指导方针制定中的一致性和透明度.
科学领域:
- 健康研究方法的方法论.
- 决策分析 决策分析
- 证据综合研究
背景情况:
- 决策框架的证据对于指导方针的制定至关重要.
- 目前的EtD框架缺乏标准化值来判断健康结果的大小 (微不足道,小,中等,大).
- 这种缺失妨碍了对效果大小和决策的客观解释.
研究的目的:
- 实证地推导出决策值 (DTs) 来证明决策 (EtD) 的判断.
- 为解释健康益处和危害的程度提供客观参考.
- 提高指导方针制定中的一致性和透明度.
主要方法:
- 方法学随机对照试验,涉及多种利益相关者.
- 使用随机分配的案例场景引发效果大小判断.
- 从收集的参与者数据中推导经验性DTs.
- 通过与历史指南判断进行比较来验证DTs.
主要成果:
- 在各种条件和结果中,经验性推导的DTs已经成功地开发出来.
- 研究结果支持不同效果大小的不同DT的假设.
- 结果表明,评级者判断与绝对效果指标之间存在关联.
- 介绍了利用衍生DTs的实用方法.
结论:
- 经验性推导的DTs有效地对EtDs的判断进行了歧视.
- 在系统性审查中,DTs可以标准化对理想和不理想的健康影响的判断.
- 这些门促进了指导方针制定过程中的一致性和透明度.
- 对于GRADE (建议评估,开发和评估的分级) 等框架来说,DT特别重要.
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