The data collection form is a bridge in-between the original studies and the final systematic reviews. It’s the basis for data analyses, directly related to the results and conclusions of systematic reviews, and plays an important role in systematic reviews. There are strict requirements of data collection forms in making Cochrane systematic reviews. In this article, the authors introduce their experiences regarding to the design of data collection form.
Sample size, mean and standard deviation are necessary when conducting meta-analysis for continuous outcomes. Advanced methods of data extraction were needed if the mean and the standard deviation couldn’t be obtained from a literature directly. Eight methods were introduced and two examples were given to illustrate how to apply the methods.
Receiving the amount of true positives, false positives, false negatives and true negatives is necessary when conducting meta-analysis of diagnostic tests. Advanced methods of data extraction are required if these data could not be directly obtained from a literature. We introduced three methods and discussed the theories. An example was then given to illustrate how to apply the methods.
Systematic reviews can provide important evidence support for clinical practice and health decision-making. In this process, literature screening and data extraction are extensively time-consuming procedures. Natural language processing (NLP), as one of the research directions of computer science and artificial intelligence, can accelerate the process of literature screening and data extraction in systematic reviews. This paper introduced the requirements of systematic reviews for rapid literature screening and data extraction, the development of NLP and types of machine learning; and systematically collated the NLP tools for the title and abstract screening, full-text screening and data extraction in systematic reviews; and discussed the problems in the application of NLP tools in the field of systematic reviews and proposed a prospect for its future development.