Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis. Issue 44 (30th October 2020)

• Record Type: Journal Article
• Authors: Kuo, Yu-Chi; Chien, Tsair-Wei; Kuo, Shu-Chun; Yeh, Yu-Tsen; Lin, Jui-Chung John; Fong, Yao
• Other Names: Khoshnood. Ardavan section editor.
• Is Part Of: Medicine. Volume 99:Issue 44(2020)
• Publication Date: 2020-10-30
• Subjects: bibliometric -- citation analysis -- dashboard -- medical subject heading -- social network analysis; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000022885
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: Background: Publications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to provide a visualization dashboard for the 100 top-cited articles related to article types and inspect major medical subject headings (i.e., MeSH terms in PubMed) to help predict citations. Methods: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations. Results: The citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients = 0.49, t = 5.62). Conclusion: The breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines. Abstract : Supplemental Digital Content is available in the text