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Living Systematic Reviews: 2. Combining Human and Machine Effort

Thomas, J; Noel-Storr, A; Marshall, I; Wallace, B; McDonald, S; Mavergames, C; Glasziou, P; ... Living Systematic Review Network, .; + view all (2017) Living Systematic Reviews: 2. Combining Human and Machine Effort. Journal of Clinical Epidemiology , 91 31 -37. 10.1016/j.jclinepi.2017.08.011. Green open access

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Abstract

New approaches to evidence synthesis, which utilise human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities ('crowds') as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential - and limitations - of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human / machine 'technologies' are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.

Type: Article
Title: Living Systematic Reviews: 2. Combining Human and Machine Effort
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jclinepi.2017.08.011
Publisher version: http://doi.org/10.1016/j.jclinepi.2017.08.011
Language: English
Additional information: © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Automaton, citizen science, crowdsourcing, machine learning, systematic review, text mining
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
URI: https://discovery.ucl.ac.uk/id/eprint/1575759
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