scholarly journals A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools

2021 ◽  
Vol 11 (12) ◽  
pp. 5527
Author(s):  
Tatiana Person ◽  
Iván Ruiz-Rube ◽  
José Miguel Mota ◽  
Manuel Jesús Cobo ◽  
Alexey Tselykh ◽  
...  
Keyword(s):  
Systematic Review ◽  
Review Process ◽  
State Of The Art ◽  
Evidence Synthesis ◽  
Comprehensive Analysis ◽  
Synthesis Process ◽  
Evidence Based ◽  
Computer Based ◽  
Comprehensive Framework ◽  
Systematic Review Process

Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review tool focused on Computer Science, to implement evidence-based systematic review processes in other disciplines.

Externalising tacit knowledge of the systematic review process

IET Software ◽  
2013 ◽  
Vol 7 (6) ◽  
pp. 298-307 ◽  
Author(s):  
Sandra Camargo Pinto Ferraz Fabbri ◽  
Katia Romero Felizardo ◽  
Fabiano Cutigi Ferrari ◽  
Elis Cristina Montoro Hernandes ◽  
Fábio Roberto Octaviano ◽  
...  
Keyword(s):  
Systematic Review ◽  
Tacit Knowledge ◽  
Review Process ◽  
Systematic Review Process

scholarly journals Bias Due to Changes in Specified Outcomes during the Systematic Review Process

PLoS ONE ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. e9810 ◽  
Author(s):  
Jamie J. Kirkham ◽  
Doug G. Altman ◽  
Paula R. Williamson
Keyword(s):  
Systematic Review ◽  
Review Process ◽  
Systematic Review Process

scholarly journals Addressing challenges with systematic review teams through effective communication: a case report

2021 ◽  
Vol 109 (4) ◽  
Author(s):  
Linda C. O’Dwyer ◽  
Q. Eileen Wafford
Keyword(s):  
Systematic Review ◽  
Case Report ◽  
Review Process ◽  
Communication Strategies ◽  
Core Competency ◽  
Effective Communication ◽  
Low Energy ◽  
Full Participation ◽  
Systematic Review Process

Background: Every step in the systematic review process has challenges, ranging from resistance by review teams to adherence to standard methodology to low-energy commitment to full participation. These challenges can derail the project and result in significant delays, duplication of work, and failure to complete the review. Communication during the systematic review process is key to ensuring it runs smoothly and is identified as a core competency for librarians involved in systematic reviews.Case Presentation: This case report presents effective communication approaches that our librarians employ to address challenges encountered while working with systematic review teams. The communication strategies we describe engage teams through information, questions, and action items and lead to productive collaborations with publishable systematic reviews.Conclusions: Effective communication with review teams keeps systematic review projects moving forward. The techniques covered in this case study strive to minimize misunderstandings, educate collaborators, and, in our experience, have led to multiple successful collaborations and publications. Librarians working in the systematic review space will recognize these challenges and can adapt these techniques to their own environments.

scholarly journals PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis

2021 ◽  
Author(s):  
Neal R Haddaway ◽  
Matthew J Page ◽  
Christopher C Pritchard ◽  
Luke A McGuinness
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Science Communication ◽  
Review Process ◽  
Evidence Synthesis ◽  
R Package ◽  
Web Based ◽  
Shiny App ◽  
Data Files ◽  
Flow Diagrams

Background Reporting standards, such as PRISMA aim to ensure that the methods and results of systematic reviews are described in sufficient detail to allow full transparency. Flow diagrams in evidence syntheses allow the reader to rapidly understand the core procedures used in a review and examine the attrition of irrelevant records throughout the review process. Recent research suggests that use of flow diagrams in systematic reviews is poor and of low quality and called for standardised templates to facilitate better reporting in flow diagrams. The increasing options for interactivity provided by the Internet gives us an opportunity to support easy-to-use evidence synthesis tools, and here we report on the development of tools for the production of PRISMA 2020-compliant systematic review flow diagrams. Methods and Findings We developed a free-to-use, Open Source R package and web-based Shiny app to allow users to design PRISMA flow diagrams for their own systematic reviews. Our tools allow users to produce standardised visualisations that transparently document the methods and results of a systematic review process in a variety of formats. In addition, we provide the opportunity to produce interactive, web-based flow diagrams (exported as HTML files), that allow readers to click on boxes of the diagram and navigate to further details on methods, results or data files. We provide an interactive example here; https://driscoll.ntu.ac.uk/prisma/. Conclusions We have developed a user-friendly suite of tools for producing PRISMA 2020-compliant flow diagrams for users with coding experience and, importantly, for users without prior experience in coding by making use of Shiny. These free-to-use tools will make it easier to produce clear and PRISMA 2020-compliant systematic review flow diagrams. Significantly, users can also produce interactive flow diagrams for the first time, allowing readers of their reviews to smoothly and swiftly explore and navigate to further details of the methods and results of a review. We believe these tools will increase use of PRISMA flow diagrams, improve the compliance and quality of flow diagrams, and facilitate strong science communication of the methods and results of systematic reviews by making use of interactivity. We encourage the systematic review community to make use of these tools, and provide feedback to streamline and improve their usability and efficiency.

scholarly journals Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuelun Zhang ◽  
Siyu Liang ◽  
Yunying Feng ◽  
Qing Wang ◽  
Feng Sun ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Digital Library ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Test Accuracy ◽  
Evidence Based ◽  
Medical Evidence ◽  
Evidence Collection ◽  
Automatic Methods

Abstract Background Systematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies. Methods An electronic search will be conducted using PubMed, Embase, ACM Digital Library, and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, and the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed. Discussion Automating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis and help to inspire further studies in this field. Systematic review registration PROSPERO CRD42020170815 (28 April 2020).

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