Donnelly CA, Boyd I, Campbell P, Craig C, Vallance P, Walport M, Whitty CJM, Woods E, Wormald C. Four principles to make evidence synthesis more useful for policy. Nature. 2018;558(7710):361–4. https://doi.org/10.1038/d41586-018-05414-4.
Article CAS PubMed Google Scholar
Saldanha, I. J., Adam, G. P., Schmid, C. H., Trikalinos, T. A., & Konnyu, K. J. (2023). Modernizing evidence synthesis for evidence-based medicine. In Clinical Decision Support and beyond: Progress and Opportunities in Knowledge-Enhanced Health and Healthcare (pp. 257–278). Elsevier. https://doi.org/10.1016/B978-0-323-91200-6.00006-1
Surkovic E, Vigar D. Scientific advice for policymakers on climate change: the role of evidence synthesis. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2022;380(2221):20210147. https://doi.org/10.1098/rsta.2021.0147.
Lefebvre, C., Glanville, J., Briscoe, S., Featherstone, R., Metzendorf, M.-I., Noel-Storr, A., Paynter, R., Rader, T., Thomas, J., & Wieland, L. (2023). Chapter 4: Searching for and selecting studies. In J. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. Page, & V. Welch, Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4 (updated October 2023)). https://training.cochrane.org/handbook/current/chapter-04
Higgins, J., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M., & Welch, V. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). John Wiley & Sons.
Bornmann L, Mutz R. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J Am Soc Inf Sci. 2015;66(11):2215–22. https://doi.org/10.1002/asi.23329.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: early experiments with GPT-4 (arXiv:2303.12712). arXiv. https://doi.org/10.48550/arXiv.2303.12712
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models (arXiv:2302.13971). arXiv. https://doi.org/10.48550/arXiv.2302.13971
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., … Rush, A. (2020). Transformers: state-of-the-art natural language processing. In Q. Liu & D. Schlangen (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 38–45). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-demos.6
Chappell M, Edwards M, Watkins D, Marshall C, Graziadio S. Machine learning for accelerating screening in evidence reviews. Cochrane Evidence Synthesis and Methods. 2023;1(5): e12021. https://doi.org/10.1002/cesm.12021.
Michelson M, Reuter K. The significant cost of systematic reviews and meta-analyses: a call for greater involvement of machine learning to assess the promise of clinical trials. Contemporary Clinical Trials Communications. 2019;16: 100443. https://doi.org/10.1016/j.conctc.2019.100443.
Article PubMed PubMed Central Google Scholar
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. L. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3(2), Article 2. https://doi.org/10.1038/s42256-020-00287-7
Cohen AM, Hersh WR, Peterson K, Yen P-Y. Reducing workload in systematic review preparation using automated citation classification. J Am Med Inform Assoc. 2006;13(2):206–19.
Article CAS PubMed PubMed Central Google Scholar
Cormack, G. V., & Grossman, M. R. (2014). Evaluation of machine-learning protocols for technology-assisted review in electronic discovery. Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 153–162. https://doi.org/10.1145/2600428.2609601
Lewis, D. D., Gray, L., & Noel, M. (2023). Confidence sequences for evaluating one-phase technology-assisted review. Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, 131–140. https://doi.org/10.1145/3594536.3595167
O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4(1):5. https://doi.org/10.1186/2046-4053-4-5.
Article PubMed PubMed Central Google Scholar
Hamel C, Hersi M, Kelly SE, Tricco AC, Straus S, Wells G, Pham B, Hutton B. Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses. BMC Med Res Methodol. 2021;21(1):285. https://doi.org/10.1186/s12874-021-01451-2.
Article PubMed PubMed Central Google Scholar
Callaghan, M., & Müller-Hansen, F. (2020). Statistical stopping criteria for automated screening in systematic reviews. Systematic Reviews. https://doi.org/10.21203/rs.2.18218/v2
Howard BE, Phillips J, Tandon A, Maharana A, Elmore R, Mav D, Sedykh A, Thayer K, Merrick BA, Walker V, Rooney A, Shah RR. SWIFT-Active Screener: accelerated document screening through active learning and integrated recall estimation. Environ Int. 2020;138: 105623. https://doi.org/10.1016/j.envint.2020.105623.
Article PubMed PubMed Central Google Scholar
Boetje J, van de Schoot R. The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses. Syst Rev. 2024;13(1):81. https://doi.org/10.1186/s13643-024-02502-7.
Article PubMed PubMed Central Google Scholar
Lefebvre, C., Glanville, J., Briscoe, S., A Littlewood, Marshall, C., Metzendorf, M.-I., Noel-Storr, A., Rader, T., Shokraneh, F., Thomas, J., & Wieland, L. (2019). Chapter 4: Searching for and selecting studies. In J. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. Page, & V. Welch, Cochrane Handbook for Systematic Reviews of Interventions (Version 6 (updated October 2019)). https://training.cochrane.org/handbook/current/chapter-04
MacDonald H, Comer C, Foster M, Labelle PR, Marsalis S, Nyhan K, Premji Z, Rogers M, Splenda R, Stansfield C, Young S. Searching for studies: a guide to information retrieval for Campbell systematic reviews. Campbell Syst Rev. 2024;20(3): e1433. https://doi.org/10.1002/cl2.1433.
Article PubMed PubMed Central Google Scholar
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Molinari A, Esuli A. SALτ: efficiently stopping TAR by improving priors estimates. Data Min Knowl Disc. 2024;38(2):535–68. https://doi.org/10.1007/s10618-023-00961-5.
Sneyd, A., & Stevenson, M. (2019). Modelling stopping criteria for search results using poisson processes. In K. Inui, J. Jiang, V. Ng, & X. W. 0001 (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, hong kong, china, november 3–7, 2019 (pp. 3482–3487). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1351
Stevenson M, Bin-Hezam R. Stopping methods for technology assisted reviews based on point processes. ACM Transactions on Information Systems. 2024;42(3):1–37. https://doi.org/10.1145/3631990.
Walton, A. (2023, January 6). Covidence product updates and bug fixes. Covidence. https://www.covidence.org/blog/release-notes-december-2022-machine-learning/
How to stop screening? · asreview/asreview · Discussion #557. (n.d.). GitHub. Retrieved 12 October 2023, from https://github.com/asreview/asreview/discussions/557
The Systematic Review Toolbox. (n.d.). Retrieved 12 October 2023, from http://systematicreviewtools.com/software.php
Jonnalagadda S, Petitti D. A new iterative method to reduce workload in systematic review process. Int J Comput Biol Drug Des. 2013;6(1–2):5–17. https://doi.org/10.1504/IJCBDD.2013.052198.
Article PubMed PubMed Central Google Scholar
Przybyła P, Brockmeier AJ, Kontonatsios G, Le Pogam M-A, McNaught J, von Elm E, Nolan K, Ananiadou S. Prioritising references for systematic reviews with RobotAnalyst: a user study. Research Synthesis Methods. 2018;9(3):470–88. https://doi.org/10.1002/jrsm.1311.
Article PubMed PubMed Central Google Scholar
Kusa, W., Zuccon, G., Knoth, P., & Hanbury, A. (2023). Outcome-based evaluation of systematic review automation. Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 125–133. https://doi.org/10.1145/3578337.3605135
Marshall IJ, Marshall R, Wallace BC, Brassey J, Thomas J. Rapid reviews may produce different results to systematic reviews: a meta-epidemiological study. J Clin Epidemiol. 2019;109:30–41. https://doi.org/10.1016/j.jclinepi.2018.12.015.
Article PubMed PubMed Central Google Scholar
Scholer, F., Kelly, D., Wu, W.-C., Lee, H. S., & Webber, W. (2013). The effect of threshold priming and need for cognition on relevance calibration and assessment. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 623–632. https://doi.org/10.1145/2484028.2484090
Stansfield C, Stokes G, Thomas J. Applying machine classifiers to update searches: analysis from two case studies. Research Synthesis Methods. 2022;13(1):121–33. https://doi.org/10.1002/jrsm.1537.
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