Abstract
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 to standard practice.Results:We found that the customised graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, 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.
Highlights
IntroductionDo you have a reference for this sentence: ‘This can make the process very time consuming, evenDo you have a reference for this sentence: ‘This can make the process very time consuming, even for small reviews; the labour required is obviously compounded in very large reviews, such as those seen in preclinical research.’ Information provided later on, about 6000 studies for a corresponding research could be provided here in the Introduction, i.e. moved from its current position in the manuscript. -It would be beneficial to explain in the introduction what is the motive for this work when free and easy-to-use tools such as Plot Digitizer exist? Please elaborate further would be a motivation to make ‘a tool which is designed for data extraction in the context of systematic reviews’
As there are no tools for graphical data extraction that have been developed for systematic reviewers to use, we developed a pilot tool for evaluation purposes that is described below
Developing the web-based tool for graphical data extraction We developed requirements for the graphical data extraction tool and chose a browser-based solution for ease of deployment during evaluation and because, should that evaluation prove positive, the code could be integrated within web-based systematic review software such as those mentioned above
Summary
Do you have a reference for this sentence: ‘This can make the process very time consuming, evenDo you have a reference for this sentence: ‘This can make the process very time consuming, even for small reviews; the labour required is obviously compounded in very large reviews, such as those seen in preclinical research.’ Information provided later on, about 6000 studies for a corresponding research could be provided here in the Introduction, i.e. moved from its current position in the manuscript. -It would be beneficial to explain in the introduction what is the motive for this work when free and easy-to-use tools such as Plot Digitizer exist? Please elaborate further would be a motivation to make ‘a tool which is designed for data extraction in the context of systematic reviews’. Studies within a review can present relevant outcome data in different ways, whether it be through providing multiple measures of the same outcome, measures at multiple timepoints, or in multiple statistical forms These variations require skill and attention from the analyst to determine which data points need to be extracted and included in the analysis, in such a way that minimises bias and error in the selection and extraction of data. This can make the process very time consuming, even for small reviews; the labour required is obviously compounded in very large reviews, such as those seen in preclinical research
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