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开展高质量系统评价的方法学标准。

Methodological Standards for Conducting High-Quality Systematic Reviews.

作者信息

De Cassai Alessandro, Dost Burhan, Tulgar Serkan, Boscolo Annalisa

机构信息

Department of Medicine (DIMED), University of Padua, 35127 Padua, Italy.

Institute of Anesthesia and Intensive Care, University Hospital of Padua, 35127 Padua, Italy.

出版信息

Biology (Basel). 2025 Aug 1;14(8):973. doi: 10.3390/biology14080973.

Abstract

Systematic reviews are a cornerstone of evidence-based research, providing comprehensive summaries of existing studies to answer specific research questions. This article offers a detailed guide to conducting high-quality systematic reviews in biology, health and social sciences. It outlines key steps, including developing and registering a protocol, designing comprehensive search strategies, and selecting studies through a screening process. The article emphasizes the importance of accurate data extraction and the use of validated tools to assess the risk of bias across different study designs. Both meta-analysis (quantitative approach) and narrative synthesis (qualitative approach) are discussed in detail. The guide also highlights the use of frameworks, such as GRADE, to assess the certainty of evidence and provides recommendations for clear and transparent reporting in line with the PRISMA 2020 guidelines. This paper aims to adapt and translate evidence-based review principles, commonly applied in clinical research, into the context of biological sciences. By highlighting domain-specific methodologies, challenges, and resources, we provide tailored guidance for researchers in ecology, molecular biology, evolutionary biology, and related fields in order to conduct transparent and reproducible evidence syntheses.

摘要

系统评价是循证研究的基石,它对现有研究进行全面总结,以回答特定的研究问题。本文提供了一份在生物学、健康和社会科学领域进行高质量系统评价的详细指南。它概述了关键步骤,包括制定和注册方案、设计全面的检索策略,以及通过筛选过程选择研究。本文强调了准确的数据提取以及使用经过验证的工具来评估不同研究设计中偏倚风险的重要性。详细讨论了荟萃分析(定量方法)和叙述性综述(定性方法)。该指南还强调了使用如GRADE等框架来评估证据的确定性,并根据PRISMA 2020指南提供清晰透明报告的建议。本文旨在将临床研究中常用的循证综述原则调整并转化到生物科学背景中。通过突出特定领域的方法、挑战和资源,我们为生态学、分子生物学、进化生物学及相关领域的研究人员提供量身定制的指导,以便进行透明且可重复的证据综合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de96/12383630/e8b8402e09d8/biology-14-00973-g001.jpg

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