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This paper explores the use of machine learning techniques to help navigate the systematic review process. Machine learning has previously been used to reliably “screen” articles for review – that is, identify relevant articles based on reviewers ’ inclusion criteria. The application of machine learning techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We, therefore, set out to develop a series of tools that would assist in the profiling and analysis of 1952 publications on the theme of “outcomes-based contracting.” Tools were developed for the following tasks: assigning publications into “policy area” categories; identifying and extracting key information for evidence mapping, such as organizations, laws, and geographical information; connecting the evidence base to an existing dataset on the same topic; and identifying subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of machine learning techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Beyond this, our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While machine learning techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analyzing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.  \nPolicy Significance Statement  \nSystematic reviews, despite aiming to bolster evidence-based policy, often face challenges in linking research to practice due to their time-intensive nature and narrow scope. Using the example of a large-scale systematic review consisting of highly heterogeneous and complex social science literature, this research highlights the potential of machine learning tools to speed up the efficacy of systematic reviews and to broaden their analytical  \n  This research article was awarded Open Data and Open Materials badges for transparent practices. See the Data Availability Statement for details.  \n©The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms ofthe Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.  \n[https://doi.org/10.1017/dap.2024.33](https://doi.org/10.1017/dap.2024.33) Published online by Cambridge Un","cbCaicsComTqET4w","https://ap.wps.com/l/cbCaicsComTqET4w","pdf",1030448,"English","# Introduction\n## Machine learning tools for systematic review support\n# Methods and tool development\n## Policy area categorization\n## Evidence mapping information extraction\n## Dataset linkage and thematic subgroup identification\n# Results and policy implications","[{\"question\":\"What problem does SyROCCo address in systematic reviews?\",\"answer\":\"The paper targets the growing time and resource burden of systematic reviews as the number of publications increases. It also addresses difficulties in maintaining timely, reliable evidence.\"},{\"question\":\"Which stages of the systematic review does the paper focus on?\",\"answer\":\"It goes beyond article screening by developing machine-learning tools for later stages, including data extraction and evidence mapping.\"},{\"question\":\"How do the tools support evidence mapping in the outcomes-based contracting literature?\",\"answer\":\"They assign publications to policy area categories, extract key evidence-mapping information such as organizations, laws, and geographical details, connect the evidence base to an existing dataset, and identify thematic subgroups.\"}]","SyROCCo - enhancing systematic reviews using machine learning | PDF",1789640916]