Background: Numerous studies have explored the most productive and influential authors in a specific field. However, 2 challenges arise when conducting such research. First, some authors may have identical names in the study data, and second, the contributions of coauthors may vary in the article by line, requiring consideration. Failure to address these issues may result in biased research findings. Our objective was to illustrate how the author-weighted scheme (AWS) and betweenness centrality (BC) can be employed to identify the 10 most frequently cited authors in a particular journal and analyze their research themes.

Methods: We collected 24,058 abstracts from the PubMed library between 2000 and 2020 using the keyword "Medicine [Journal]." Author names, countries/regions, and medical subject headings (MeSH terms) were collected. The AWS to identify the top 10 authors with a higher x-index was applied. To address the issue of authors with identical names affiliated with different research institutes, we utilized the BC method. Social network analysis (SNA) was conducted, and 10 major clusters were identified to highlight authors with a higher x-index within the corresponding clusters. We utilized SNA to analyze the MeSH terms from articles of the 10 top-cited authors to identify their research themes.

Results: Our findings revealed the following: within the top 10 cited authors, 2 authors from China shared identical names with Jing Li and Tao-Wang; JA Winkelstein from Maryland (US) had the highest x-index (15.58); Chia-Hung Kao from Taiwan was the most prolific author, having published 115 articles in Medicine since 2003; and the 3 primary research themes, namely, complications, etiology, and epidemiology, were identified using MeSH terms from the 10 most frequently cited authors.

Conclusions: Using AWS and BC, we identified the top 10 most cited authors. The research methods we utilized in this study (BC and AWS) have the potential to be applied to other bibliometric analyses in the future.