TY - JOUR
T1 - Living systematic reviews
T2 - 2. Combining human and machine effort
AU - Living Systematic Review Network
AU - Living Systematic Review Network
AU - Thomas, James
AU - Noel-Storr, Anna
AU - Marshall, Iain
AU - Wallace, Byron
AU - McDonald, Steven
AU - Mavergames, Chris
AU - Glasziou, Paul
AU - Shemilt, Ian
AU - Synnot, Anneliese
AU - Turner, Tari
AU - Elliott, Julian
AU - Agoritsas, Thomas
AU - Hilton, John
AU - Perron, Caroline
AU - Akl, Elie
AU - Hodder, Rebecca
AU - Pestridge, Charlotte
AU - Albrecht, Lauren
AU - Horsley, Tanya
AU - Platt, Joanne
AU - Armstrong, Rebecca
AU - Nguyen, Phi Hung
AU - Plovnick, Robert
AU - Arno, Anneliese
AU - Ivers, Noah
AU - Quinn, Gail
AU - Au, Agnes
AU - Johnston, Renea
AU - Rada, Gabriel
AU - Bagg, Matthew
AU - Jones, Arwel
AU - Ravaud, Philippe
AU - Boden, Catherine
AU - Kahale, Lara
AU - Richter, Bernt
AU - Boisvert, Isabelle
AU - Keshavarz, Homa
AU - Ryan, Rebecca
AU - Brandt, Linn
AU - Kolakowsky-Hayner, Stephanie A.
AU - Salama, Dina
AU - Brazinova, Alexandra
AU - Nagraj, Sumanth Kumbargere
AU - Salanti, Georgia
AU - Buchbinder, Rachelle
AU - Lasserson, Toby
AU - Santaguida, Lina
AU - Champion, Chris
AU - Lawrence, Rebecca
AU - Santesso, Nancy
N1 - Publisher Copyright:
© 2017 The Authors
PY - 2017/11/1
Y1 - 2017/11/1
N2 - 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.
AB - 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.
KW - Automation
KW - Citizen science
KW - Crowdsourcing
KW - Machine learning
KW - Systematic review
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85028965305&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2017.08.011
DO - 10.1016/j.jclinepi.2017.08.011
M3 - Review article
C2 - 28912003
AN - SCOPUS:85028965305
SN - 0895-4356
VL - 91
SP - 31
EP - 37
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -