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如何解读一项荟萃分析？

How to interpret a meta-analysis?

作者信息

El Dib Regina

机构信息

Universidade Estadual Paulista - UNESP, Instituto de Ciência e Tecnologia, São Paulo, SP, Brasil.

McMaster University, Departamento de Métodos, Evidências e Impacto de Pesquisa em Saúde - HEI, Hamilton, ON, Canada.

出版信息

J Vasc Bras. 2022 Oct 3;21:e20220043. doi: 10.1590/1677-5449.202200431. eCollection 2022.

DOI:10.1590/1677-5449.202200431
PMID:36259053
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9536318/
Abstract

There is an enormous and ever-growing quantity of healthcare information available and practitioners must transform it into knowledge to be able to use it in their clinical practice. Even readers who do not conduct scientific studies themselves need to understand the scientific method in detail to be able to critically evaluate scientific articles. Evidence-based healthcare (EBH) can be defined as the link between good scientific research and clinical practice and systematic reviews constitute one of the forms of research excellence proposed within EBH. Systematic reviews employ rigorous methods that reduce the occurrence of bias. Systematic reviews with meta-analyses generally optimize the results found, because quantitative analysis of the studies included in the review yields additional information. In this paper, we will discuss how to interpret a meta-analysis and how to apply subset and sensitivity analysis strategies and we will also describe possible sources of heterogeneity and common errors that can affect a meta-analysis.

摘要

现在有大量且不断增长的医疗保健信息可供使用，从业者必须将其转化为知识，以便能够在临床实践中运用。即使是那些自己不进行科学研究的读者，也需要详细了解科学方法，以便能够批判性地评估科学文章。循证医疗保健（EBH）可以定义为良好的科学研究与临床实践之间的联系，而系统评价是循证医疗保健中提出的卓越研究形式之一。系统评价采用严格的方法，减少偏差的发生。进行荟萃分析的系统评价通常会优化所发现的结果，因为对纳入评价的研究进行定量分析会产生额外的信息。在本文中，我们将讨论如何解读荟萃分析以及如何应用亚组分析和敏感性分析策略，我们还将描述可能影响荟萃分析的异质性来源和常见错误。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/a6a24f2dcede/jvb-21-e20220043-g05.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/933195516f85/jvb-21-e20220043-g03.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/a6a24f2dcede/jvb-21-e20220043-g05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/d059d7130a73/jvb-21-e20220043-g01-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/3eab1040e582/jvb-21-e20220043-g02-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/dad63e6a096d/jvb-21-e20220043-g03-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/55c89bf12879/jvb-21-e20220043-g04-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/65b379213986/jvb-21-e20220043-g05-en.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/9875e928a3ac/jvb-21-e20220043-g01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/3eab1040e582/jvb-21-e20220043-g02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/933195516f85/jvb-21-e20220043-g03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/92614ed6335f/jvb-21-e20220043-g04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c56/9536318/a6a24f2dcede/jvb-21-e20220043-g05.jpg

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