Lee Young Ji, Arida Janet A, Donovan Heidi S
Department of Health and Community Systems, School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Cancer Med. 2017 Nov;6(11):2595-2605. doi: 10.1002/cam4.1165. Epub 2017 Sep 29.
Crowdsourcing is "the practice of obtaining participants, services, ideas, or content by soliciting contributions from a large group of people, especially via the Internet." (Ranard et al. J. Gen. Intern. Med. 29:187, 2014) Although crowdsourcing has been adopted in healthcare research and its potential for analyzing large datasets and obtaining rapid feedback has recently been recognized, no systematic reviews of crowdsourcing in cancer research have been conducted. Therefore, we sought to identify applications of and explore potential uses for crowdsourcing in cancer research. We conducted a systematic review of articles published between January 2005 and June 2016 on crowdsourcing in cancer research, using PubMed, CINAHL, Scopus, PsychINFO, and Embase. Data from the 12 identified articles were summarized but not combined statistically. The studies addressed a range of cancers (e.g., breast, skin, gynecologic, colorectal, prostate). Eleven studies collected data on the Internet using web-based platforms; one recruited participants in a shopping mall using paper-and-pen data collection. Four studies used Amazon Mechanical Turk for recruiting and/or data collection. Study objectives comprised categorizing biopsy images (n = 6), assessing cancer knowledge (n = 3), refining a decision support system (n = 1), standardizing survivorship care-planning (n = 1), and designing a clinical trial (n = 1). Although one study demonstrated that "the wisdom of the crowd" (NCI Budget Fact Book, 2017) could not replace trained experts, five studies suggest that distributed human intelligence could approximate or support the work of trained experts. Despite limitations, crowdsourcing has the potential to improve the quality and speed of research while reducing costs. Longitudinal studies should confirm and refine these findings.
众包是“通过向一大群人征求贡献来获取参与者、服务、想法或内容的做法,尤其是通过互联网”。(拉纳德等人,《普通内科医学杂志》29:187,2014年)尽管众包已被应用于医疗保健研究,且其分析大型数据集和快速获得反馈的潜力最近已得到认可,但尚未对癌症研究中的众包进行系统评价。因此,我们试图确定众包在癌症研究中的应用并探索其潜在用途。我们使用PubMed、CINAHL、Scopus、PsychINFO和Embase对2005年1月至2016年6月发表的关于癌症研究中众包的文章进行了系统评价。对12篇已识别文章的数据进行了总结,但未进行统计合并。这些研究涉及多种癌症(如乳腺癌、皮肤癌、妇科癌症、结直肠癌、前列腺癌)。11项研究使用基于网络的平台在互联网上收集数据;1项研究在购物中心使用纸笔数据收集方式招募参与者。4项研究使用亚马逊土耳其机器人进行招募和/或数据收集。研究目标包括对活检图像进行分类(n = 6)、评估癌症知识(n = 3)、完善决策支持系统(n = 1)、规范生存护理计划(n = 1)以及设计一项临床试验(n = 1)。尽管有一项研究表明“群体智慧”(《美国国家癌症研究所预算手册》,2017年)无法取代训练有素的专家,但有5项研究表明,分布式人类智能可以接近或支持训练有素的专家的工作。尽管存在局限性,但众包有潜力提高研究质量和速度,同时降低成本。纵向研究应证实并完善这些发现。
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