{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-open-source-integrated-framework-for-the","title":"An open-source integrated framework for the automation of citation collection and screening in systematic reviews","arxiv_id":"2202.10033","date":"2022-02-21","proceeding":null,"authors":["Angelo D'Ambrosio","Hajo Grundmann","Tjibbe Donker"],"abstract":"The exponential growth of scientific production makes secondary literature abridgements increasingly demanding. We introduce a new open-source framework for systematic reviews that significantly reduces time and workload for collecting and screening scientific literature. The framework provides three main tools: 1) an automatic citation search engine and manager that collects records from multiple online sources with a unified query syntax, 2) a Bayesian, active machine learning, citation screening tool based on iterative human-machine interaction to increase predictive accuracy and, 3) a semi-automatic, data-driven query generator to create new search queries from existing citation data sets. To evaluate the automatic screener's performance, we estimated the median posterior sensitivity and efficiency [90% Credible Intervals] using Bayesian simulation to predict the distribution of undetected potentially relevant records. Tested on an example topic, the framework collected 17,755 unique records through the citation manager; 766 records required human evaluation while the rest were excluded by the automatic classifier; the theoretical efficiency was 95.6% [95.3%, 95.7%] with a sensitivity of 100% [93.5%, 100%]. A new search query was generated from the labelled dataset, and 82,579 additional records were collected; only 567 records required human review after automatic screening, and six additional positive matches were found. The overall expected sensitivity decreased to 97.3% [73.8%, 100%] while the efficiency increased to 98.6% [98.2%, 98.7%]. The framework can significantly reduce the workload required to conduct large literature reviews by simplifying citation collection and screening while demonstrating exceptional sensitivity. Such a tool can improve the standardization and repeatability of systematic reviews.","url_abs":"https://arxiv.org/abs/2202.10033v2","url_pdf":"https://arxiv.org/pdf/2202.10033v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-open-source-integrated-framework-for-the","repo_url":"https://github.com/ad-papers-material/bart_systreviewclassifier","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"an-open-source-integrated-framework-for-the","repo_url":"https://github.com/bakaburg1/baysren","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}