Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability

Nour Shaheen, Ahmed Shaheen, Alaa Ramadan, M. Hefnawy, Abdelraouf Ramadan, Ismail A. Ibrahim, Maged Elsayed Hassanein, Mohamed E. Ashour, Oliver Flouty
{"title":"Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability","authors":"Nour Shaheen, Ahmed Shaheen, Alaa Ramadan, M. Hefnawy, Abdelraouf Ramadan, Ismail A. Ibrahim, Maged Elsayed Hassanein, Mohamed E. Ashour, Oliver Flouty","doi":"10.3389/frma.2023.1268045","DOIUrl":null,"url":null,"abstract":"Systematic reviews play a crucial role in evidence-based practices as they consolidate research findings to inform decision-making. However, it is essential to assess the quality of systematic reviews to prevent biased or inaccurate conclusions. This paper underscores the importance of adhering to recognized guidelines, such as the PRISMA statement and Cochrane Handbook. These recommendations advocate for systematic approaches and emphasize the documentation of critical components, including the search strategy and study selection. A thorough evaluation of methodologies, research quality, and overall evidence strength is essential during the appraisal process. Identifying potential sources of bias and review limitations, such as selective reporting or trial heterogeneity, is facilitated by tools like the Cochrane Risk of Bias and the AMSTAR 2 checklist. The assessment of included studies emphasizes formulating clear research questions and employing appropriate search strategies to construct robust reviews. Relevance and bias reduction are ensured through meticulous selection of inclusion and exclusion criteria. Accurate data synthesis, including appropriate data extraction and analysis, is necessary for drawing reliable conclusions. Meta-analysis, a statistical method for aggregating trial findings, improves the precision of treatment impact estimates. Systematic reviews should consider crucial factors such as addressing biases, disclosing conflicts of interest, and acknowledging review and methodological limitations. This paper aims to enhance the reliability of systematic reviews, ultimately improving decision-making in healthcare, public policy, and other domains. It provides academics, practitioners, and policymakers with a comprehensive understanding of the evaluation process, empowering them to make well-informed decisions based on robust data.","PeriodicalId":73104,"journal":{"name":"Frontiers in research metrics and analytics","volume":"36 2","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in research metrics and analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frma.2023.1268045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Systematic reviews play a crucial role in evidence-based practices as they consolidate research findings to inform decision-making. However, it is essential to assess the quality of systematic reviews to prevent biased or inaccurate conclusions. This paper underscores the importance of adhering to recognized guidelines, such as the PRISMA statement and Cochrane Handbook. These recommendations advocate for systematic approaches and emphasize the documentation of critical components, including the search strategy and study selection. A thorough evaluation of methodologies, research quality, and overall evidence strength is essential during the appraisal process. Identifying potential sources of bias and review limitations, such as selective reporting or trial heterogeneity, is facilitated by tools like the Cochrane Risk of Bias and the AMSTAR 2 checklist. The assessment of included studies emphasizes formulating clear research questions and employing appropriate search strategies to construct robust reviews. Relevance and bias reduction are ensured through meticulous selection of inclusion and exclusion criteria. Accurate data synthesis, including appropriate data extraction and analysis, is necessary for drawing reliable conclusions. Meta-analysis, a statistical method for aggregating trial findings, improves the precision of treatment impact estimates. Systematic reviews should consider crucial factors such as addressing biases, disclosing conflicts of interest, and acknowledging review and methodological limitations. This paper aims to enhance the reliability of systematic reviews, ultimately improving decision-making in healthcare, public policy, and other domains. It provides academics, practitioners, and policymakers with a comprehensive understanding of the evaluation process, empowering them to make well-informed decisions based on robust data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
评估系统性综述:确保有效性和可靠性的综合指南
系统综述在循证实践中发挥着至关重要的作用,因为它们整合了研究成果,为决策提供依据。然而,评估系统综述的质量以防止出现偏颇或不准确的结论至关重要。本文强调了遵守 PRISMA 声明和 Cochrane 手册等公认指南的重要性。这些建议倡导系统性方法,并强调记录关键部分,包括检索策略和研究选择。在评估过程中,对方法、研究质量和整体证据强度进行全面评估至关重要。科克伦偏倚风险和 AMSTAR 2 核对表等工具有助于识别潜在的偏倚来源和综述局限性,如选择性报告或试验异质性。对纳入研究的评估强调提出明确的研究问题,并采用适当的检索策略来构建稳健的综述。通过精心选择纳入和排除标准,确保研究的相关性并减少偏倚。准确的数据综合,包括适当的数据提取和分析,是得出可靠结论的必要条件。荟萃分析是一种汇总试验结果的统计方法,可提高治疗效果评估的精确度。系统性综述应考虑一些关键因素,如消除偏见、披露利益冲突以及承认综述和方法的局限性。本文旨在提高系统综述的可靠性,最终改善医疗保健、公共政策和其他领域的决策。它为学者、从业人员和决策者提供了对评价过程的全面了解,使他们能够基于可靠的数据做出明智的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.50
自引率
0.00%
发文量
0
审稿时长
14 weeks
期刊最新文献
Managing technological sovereignty: a systematic review of semiconductor industry policy and regional ecosystem governance. Generative artificial intelligence in the publishing industry: adoption, use, intellectual property, and other challenges. What evidence syntheses reveal about PROSPERO, INPLASY, OSF, the Research Registry, and protocols.io: a meta-research study. Ubuntu as a blueprint: learning ethical transdisciplinarity from African indigenous knowledge systems. Data collection methods in qualitative research: researchers' reflections.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1