• Ya-Ching Tsai, Tsair-Wei Chien, Jian-Wei Wu, and Chien-Ho Lin.
• Department of Psychiatry, Kai-Suan Psychiatric Hospital, Kaohsiung, Taiwan.
• Medicine (Baltimore). 2022 Sep 16; 101 (37): e30545.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a common neuro developmental disorder that affects children and adolescents. It is estimated that the prevalence of ADHD is 7.2% throughout the world. There have been a number of articles published in the literature related to ADHD. However, it remains unclear which countries, journals, subject categories, and articles have the greatest influence. The purpose of this study was to display influential entities in 100 top-cited ADHD-related articles (T100ADHD) on an alluvial plot and apply alluvial to better understand the network characteristics of T100ADHD across entities.MethodsUsing the PubMed and Web of Science (WoS) databases, T100ADHD data since 2011 were downloaded. The dominant entities were compared using alluvial plots based on citation analysis. Based on medical subject headings (MeSH terms) and research areas extracted from PubMed and WoS, social network analysis (SNA) was performed to classify subject categories. To examine the difference in article citations among subject categories and the predictive power of MeSH terms on article citations in T100ADHD, one-way analysis of variance and regression analysis were used.ResultsThe top 3 countries (the United States, the United Kingdom, and the Netherlands) accounted for 75% of T100ADHD. The most citations per article were earned by Brazil (=415.33). The overall impact factor (IF = citations per 100) of the T100ADHD series is 188.24. The most cited article was written by Polanczyk et al from Brazil, with 772 citations since 2014. The majority of the articles were published and cited in Biol Psychiatry (13%; IF = 174.15). The SNA was used to categorize 6 subject areas. On the alluvial plots, T100ADHD's network characteristics were successfully displayed. There was no difference in article citations among subject categories (F = 1.19, P = .320). The most frequently occurring MeSH terms were physiopathology, diagnosis, and epidemiology. A significant correlation was observed between MeSH terms and the number of article citations (F = 25.36; P < .001).ConclusionDrawing the alluvial plot to display network characteristics in T100ADHD was a breakthrough. Article subject categories can be classified using MeSH terms to predict T100ADHD citations. Bibliometric analyses of 100 top-cited articles can be conducted in the future.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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