Department of Psychiatry, Kai-Suan Psychiatric Hospital, Kaohsiung, Taiwan.
Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine (Baltimore). 2022 Sep 16;101(37):e30545. doi: 10.1097/MD.0000000000030545.
BACKGROUND: Attention-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. METHODS: Using 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. RESULTS: The 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). CONCLUSION: Drawing 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.
背景：注意力缺陷/多动障碍（ADHD）是一种常见的神经发育障碍，影响儿童和青少年。据估计，全球 ADHD 的患病率为 7.2%。文献中有许多与 ADHD 相关的文章发表。然而，目前尚不清楚哪些国家、期刊、主题类别和文章的影响力最大。本研究的目的是在冲积图上展示 100 篇 ADHD 相关高引论文（T100ADHD）中具有影响力的实体，并应用冲积图更好地理解 T100ADHD 跨实体的网络特征。 方法：使用 PubMed 和 Web of Science（WoS）数据库，下载了 2011 年以来的 T100ADHD 数据。基于引文分析，使用冲积图比较主要实体。基于从 PubMed 和 WoS 中提取的医学主题词（MeSH 术语）和研究领域，进行社会网络分析（SNA）以对主题类别进行分类。为了检查主题类别之间文章引文的差异以及 MeSH 术语对 T100ADHD 中文章引文的预测能力，使用单因素方差分析和回归分析进行了研究。 结果：排名前 3 的国家（美国、英国和荷兰）占 T100ADHD 的 75%。每篇文章的平均引文数最高的是巴西（=415.33）。T100ADHD 系列的总影响因子（IF=每 100 篇文章的引文数）为 188.24。被引频次最高的文章是巴西的 Polanczyk 等人撰写的，自 2014 年以来被引 772 次。大多数文章发表在 Biol Psychiatry（13%；IF=174.15）上，并被引用。SNA 用于对 6 个主题领域进行分类。在冲积图上，成功地展示了 T100ADHD 的网络特征。各主题类别的文章引文数无差异（F=1.19，P=0.320）。最常出现的 MeSH 术语是生理病理学、诊断和流行病学。MeSH 术语与文章引文数之间存在显著相关性（F=25.36；P<.001）。 结论：绘制冲积图以显示 T100ADHD 的网络特征是一个突破。可以使用 MeSH 术语对文章主题类别进行分类，以预测 T100ADHD 的引文数。未来可以对 100 篇高引论文进行文献计量分析。
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