Fletcher, A. and Stevenson, M. orcid.org/0000-0002-9483-6006 (2026) Confidence-based stopping methods for systematic reviews. In: Moffat, A., Scholer, F., Bast, H., Najork, M. and Zhang, M., (eds.) SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, 20-24 Jul 2026, Melbourne, VIC, Australia. . Association for Computing Machinery, pp. 3721-3726. ISBN: 9798400725999.
Abstract
Technology Assisted Review stopping methods aim to ensure that no more documents are screened than necessary. Most existing approaches focus on achieving a target recall, which does not consider whether an information need has been met. This paper introduces two heuristic stopping methods that instead monitor whether screened documents contain enough information to make a decision. Evaluation on a standard dataset of Diagnostic Test Accuracy Systematic Reviews demonstrates that the proposed approaches substantially reduce the number of documents that need to be examined while, in the majority of cases, maintaining conclusions that are consistent with all evidence available.
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Editors: |
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| Copyright, Publisher and Additional Information: | © 2026 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
| Keywords: | technology-assisted review; stopping rules; systematic reviews; diagnostic test accuracy |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
| Date Deposited: | 06 May 2026 13:00 |
| Last Modified: | 22 Jul 2026 09:17 |
| Status: | Published |
| Publisher: | Association for Computing Machinery |
| Refereed: | Yes |
| Identification Number: | 10.1145/3805712.3809924 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:240793 |
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