Mapping theme trends and knowledge structure of labor analgesia: a quantitative, co-word biclustering analysis of data in 2000-2020

Abstract

Background: The distribution knowledge structure and pattern of the literature on labor analgesia in PubMed were examined. Methods: Scientific papers on labor analgesia published from 1 January, 2000 to 31 June, 2020 were retrieved. The extracted MeSH items were quantitatively analyzed by the Bibliographic Item Co-Occurrence Matrix Builder (BICOMB), and the high frequency MeSH items were identified. In gCLUTO software, repeated bisection method was used to Mountain visualisation, and the visual matrix was established. By constructing high-frequency MeSH terms co-occurrence matrix, strategic diagram and social network are further completed. Results: The search strategy yielded 2870 papers, and the number of papers published annually had changed slightly during the study period. Among all extracted MeSH terms, 42 high-frequency MeSH terms were identified by consensus, and were divided into six categories by diclustering analysis. In the strategic diagram, the methods of labor analgesia, drug doses, and routes of administration were properly presented. In contrast, statistical and numerical data on obstetric analgesia were relatively underdeveloped, and management of pain during labor was undeveloped. In the social network analysis, the position status of each component was determined by the centrality values. Conclusions: The findings on labor analgesia are relatively divergent, and the six research categories outlined in this study reflect the publication trends in the field of labor analgesia to some extent. Our quantitative bibliometric research across a 20-year span depicts the overall direction of the latest topics and provides some hints for researchers when launching new projects.