EPPI-Centre, Department of Social Science, University College London, 18 Woburn Square, London, WC1H 0NR, UK.
Radcliffe Department of Medicine, University of Oxford, Level 4, Academic Block, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
J Clin Epidemiol. 2017 Nov;91:31-37. doi: 10.1016/j.jclinepi.2017.08.011. Epub 2017 Sep 11.
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.
新的证据综合方法,将人力和机器自动化以相互增强的方式结合使用,可以提高实时系统评价的可行性和可持续性。人力是一种稀缺且宝贵的资源,在自动化不可能或不可取的情况下需要人力,包括在线社区(“众包”)的贡献,以及来自评论作者和信息专家的更传统的贡献。自动化可以协助完成一些系统评价任务,包括搜索、资格评估、全文报告的识别和检索、数据提取以及偏倚风险评估。可以开发工作流程,使人力和机器自动化都能以更有效和高效的方式相互支持,从而大大提高系统评价的效率。本文描述并讨论了在实时系统评价中开展特定任务的新方法的潜力和局限性,确定了这些人力/机器“技术”已经应用的领域,以及需要进一步研究和开发的领域。虽然本文的背景是实时系统评价,但其中许多使能技术同样适用于系统评价的标准方法。