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The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

机译:在线申请的开发和评估协助从图表中提取数据以供系统评论

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摘要

Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison with participants’ standard practice for extracting data from graphs in PDF documents. Results: We found that the customised graphical data extraction tool is not inferior to users’ (N=10) prior standard practice. Our study was not designed to show superiority, but suggests that, on average, participants saved around 6 minutes per graph using the new tool, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.
机译:背景:从初级研究的报告,在其上的系统评价结果取决于数据的提取,需要精确地进行。为了帮助可靠性,建议两位研究者独立进行数据提取。从PDF文件中的曲线图的统计数据的提取是特别具有挑战性的,因为该方法通常是完全手动的,并审阅有时需要恢复到保持尺子靠在页面到读出的值:固有耗时且容易出错的过程。方法:为了减轻一些我们整合和定制的两个现有的JavaScript库创建一个新的基于Web的图形数据提取工具,以协助评审从图表中提取数据的上述问题。该工具的目的是促进通过其可用于通过鼠标点击来提取数据的用户界面更准确和及时的数据提取。我们进行了非劣效性评估,以检查其与参与者在PDF文档从图表中提取数据的标准做法比较性能。结果:我们发现,所述定制的图形数据提取工具并不逊色于用户的(N = 10)前的标准做法。我们的研究未显示优越性,但表明,平均而言,参与者使用新的工具,伴随精度大幅提高节省约每图6分钟。结论:我们的研究表明,这种类型的在线系统评价软件工具的引入将在促进生产的准确和及时的证据的合成,提高决策是有益的。

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