‘Refbin’ an online platform to extract and classify large-scale information: a pilot study of COVID-19 related papers

Abstract

IntroductionThe number of new biomedical manuscripts published on important topics exceeds the capacity of single persons to read. Integration of literature is an even more elusive task. This article describes a pilot study of a scalable online system to integrate data from 1000 articles on COVID-19.MethodsArticles were imported from PubMed using the query ‘COVID-19’. The full text of articles reporting new data was obtained and the results extracted manually. An online software system was used to enter the results. Similar results were bundled using note fields in parent–child order. Each extracted result was linked to the source article. Each new data entry comprised at least four note fields: (1) result, (2) population or sample, (3) description of the result and (4) topic. Articles underwent iterative rounds of group review over remote sessions.ResultsScreening 4126 COVID-19 articles resulted in a selection of 1000 publications presenting new data. The results were extracted and manually entered in note fields. Integration from multiple publications was achieved by sharing parent note fields by child entries. The total number of extracted primary results was 12 209. The mean number of results per article was 15.1 (SD 12.0). The average number of parent note fields for each result note field was 6.8 (SD 1.4). The total number of all note fields was 28 809. Without sharing of parent note fields, there would have been a total of 94 986 note fields.ConclusionThis pilot study demonstrates the feasibility of a scalable online system to extract results from 1000 manuscripts. Using four types of notes to describe each result provided standardisation of data entry and information integration. There was substantial reduction in complexity and reduction in total note fields by sharing of parent note fields. We conclude that this system provides a method to scale up extraction of information on very large topics.