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Type: Journal article
Title: Living systematic reviews: 2. Combining human and machine effort
Author: Thomas, J.
Noel-Storr, A.
Marshall, I.
Wallace, B.
McDonald, S.
Mavergames, C.
Glasziou, P.
Shemilt, I.
Synnot, A.
Turner, T.
Elliott, J.
Agoritsas, T.
Hilton, J.
Perron, C.
Akl, E.
Hodder, R.
Pestridge, C.
Albrecht, L.
Horsley, T.
Platt, J.
et al.
Citation: Journal of Clinical Epidemiology, 2017; 91:31-37
Publisher: Elsevier
Issue Date: 2017
ISSN: 0895-4356
Statement of
James Thomas, Anna Noel-Storr, Iain Marshall, Byron Wallace, Steven McDonald, Chris Mavergames, Paul Glasziou, Ian Shemilt, Anneliese Synnot, Tari Turner, Julian Elliott, on behalf of the Living Systematic Review Network (Zachary Munn)
Abstract: New approaches to evidence synthesis, which use 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.
Keywords: Systematic review; automation; crowdsourcing; citizen science; machine learning; text mining
Description: Published online 11 September 2017
Rights: © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (
RMID: 0030093273
DOI: 10.1016/j.jclinepi.2017.08.011
Grant ID:
Appears in Collections:Public Health publications

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