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​​The Centre for Evaluation presents a series of four lunch time webinars on systematic review software for both people who are contemplating to use such software for the first time and those who want to learn about recent developments. In each webinar, a speaker will present the software developed by their organisation. This will be followed by a discussion in which the public can ask questions and share their personal experience with the software.  

About EPPI-Reviewer 

 is a cloud-based systematic review platform which supports a range of reviews from quantitative meta-analysis, to qualitative meta-ethnography. It contains features to categorise data and enable reviewers to keep track of decision-making pathways throughout the systematic review process. Like Covidence, it is widely used internationally and is used by both Cochrane and Campbell Collaborations. EPPI-Reviewer has been used for thousands of reviews across numerous topics including health, social care, education, criminology, international development and climate change. 

Reviewers can begin by uploading text files containing bibliographic records and by searching PubMed directly from within the software. They can then screen the records for eligibility based on their titles and abstracts, and later, on full texts (which can be uploaded into the system too). The user-interface is clear and enables contemporaneous collaboration across institutions (e.g. during double screening). Researchers can choose from a number of pre-built risk of bias and data extraction tools, or create their own templates for data extraction and synthesis. Qualitative synthesis can be assisted by ‘line-by-line’ coding of pdf files and the ability to create reports of text in customised formats. A range of options for statistical meta-analysis are also available from integration with the ‘R’ statistical software which supports standard meta-analysis with a range of models and extensions including meta-regression and network meta-analysis. 

The EPPI-Reviewer system includes automation technologies to make the reviewing process more efficient. These include ‘active learning’ for citation screening, where the software ‘learns’ to apply your inclusion / exclusion criteria as you screen, which can make the process of study identification much more efficient. It also has machine learning classifiers for identifying RCTs, systematic reviews, and economic evaluations, and allows reviewers to build their own classifiers from their own data. Finally, EPPI-Reviewer enables reviewers to publish online databases of their review data, enabling anyone to query their data and visualise their characteristics. 

Speakers

Anastasia Koryakina

Anastasia Koryakina, MSc (Oxon) is EPPI-Reviewer Support Officer at the EPPI-Centre, UCL. She provides assistance and training in using EPPI-Reviewer and review management, as well as represents the user’s perspective in the development of this software. Before joining UCL in February 2022, she was teaching foreign languages for ten years in Russia and Italy and worked at the Department of Education in Moscow, Russia. Her research interests include second language acquisition, bi- and multilingualism, and evidence synthesis in the fields of applied linguistics and language education. 

​James Thomas

​James Thomas is Professor of Social Research & Policy at the EPPI-Centre. His research is centred on improving policy and decision-making through more creative use and appreciation of existing knowledge. It covers substantive disciplinary fields – such as health promotion, public health and education – and also the development of tools and methods that support this work conducted both within University College London (UCL) and in the wider community. He has written extensively on research synthesis, including meta-analysis and methods for combining qualitative and quantitative research in ‘mixed method’ reviews; and also designed EPPI-Reviewer, software which manages data through all stages of a systematic review. 

Next webinar in series

Date TBC:
12.00 - 13.00