Menelaos Konstantinidis, Catherine Stratton, Sofia Tsokani, Julian Elliott, Mark Simmonds, Jessie McGowan, David Moher, Andrea C Tricco, Areti-Angeliki Veroniki
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引用次数: 0
Abstract
Objective: The objective of this scoping review is to describe existing guidance documents or studies reporting on the conduct of meta-analyses in updated systematic reviews (USRs) or living systematic reviews (LSRs).
Introduction: The rapid increase in the medical literature poses a substantial challenge in keeping systematic reviews up to date. In LSRs, a review is updated with a pre-specified frequency or when some other signalling criterion is triggered. While the LSR framework is well-established, there is uncertainty regarding the most appropriate methods for conducting repeated meta-analyses over time, which may result in sub-optimal decision-making.
Inclusion criteria: Studies of any design (including commentaries, books, manuals) providing guidance on conducting meta-analysis in USRs or LSRs.
Methods: This review will use the JBI methodology for scoping reviews. We will search multiple medical bibliographic databases (Cochrane Library, Embase, ERIC, MEDLINE, JBI Evidence Synthesis , and PsycINFO), statistical and mathematics databases (COBRA, Current Index to Statistics, MathSciNet, Project Euclid Complete, and zbMATH), pre print archives (Arvix, BioRxiv, and MedRxiv), and unpublished (or gray) literature sources. Two reviewers will independently screen titles, abstracts, and full-text documents, and extract data. Characteristics of recommendations for meta-analysis in USRs and LSRs will be presented using descriptive statistics and categorized concepts.
Review registration: Open Science Framework https://osf.io/9c27g.
目的：本综述的目的是描述更新系统评价（USRs）或活系统评价（lrs）中进行meta分析的现有指导文件或研究报告。引言：医学文献的快速增长对保持系统综述的更新提出了实质性的挑战。在lsr中，审查以预先指定的频率或当触发某些其他信令标准时更新。虽然LSR框架是完善的，但随着时间的推移，进行重复荟萃分析的最合适方法存在不确定性，这可能导致次优决策。纳入标准：任何设计的研究（包括评论、书籍、手册），对usr或lsr进行荟萃分析提供指导。方法：我们将使用JBI方法进行范围审查。我们将检索多个医学书目数据库（Cochrane Library、Embase、ERIC、MEDLINE、JBI Evidence Synthesis和PsycINFO）、统计和数学数据库（COBRA、Current Index to Statistics、MathSciNet、Project Euclid Complete和zbMATH）、预印本档案（Arvix、BioRxiv和MedRxiv），以及难以定位/未发表（或灰色）的文献。两名审稿人将独立筛选标题、摘要和全文文档，并提取数据。将使用描述性统计和分类概念介绍USRs和lrs的meta分析建议的特征。该审查项目的详细信息可在开放科学框架：https://osf.io/9c27g中找到。
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