Background: A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.Methods: We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date -Modification]: "2021" [Date -Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs). Results: The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34).Conclusion: Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

Background: We saw a steady increase in the number of bibliographic studies published over the years. The reason for this rise is attributed to the better accessibility of bibliographic data and software packages that specialize in bibliographic analyses. Any difference in citation achievements between bibliographic and meta-analysis studies observed so far need to be verified. In this study, we aimed to identify the frequently observed MeSH terms in these 2 types of study and investigate whether the highlighted MeSH terms are strongly associated with one of the study types.Methods: By searching the PubMed Central database, 5121 articles relevant to bibliometric and meta-analysis studies were downloaded since 2011. Social network analysis was applied to highlight the major MeSH terms of quantitative and statistical methods in these 2 types of studies. MeSH terms were then individually tested for any differences in event counts over the years between study types using odds of 95% confidence intervals for comparison.Results: In these 2 studies, we found that the most productive countries were the United States (19.9%), followed by the United Kingdom (8.8%) and China (8.7%); the most number of articles were published in PLoS One (2.9%), Stat Med (2.5%), and Res Synth (2.4%); and the most frequently observed MeSH terms were statistics and numerical data in bibliographic studies and methods in meta-analysis. Differences were found when compared to the event counts and the citation achievements in these 2 study types. Conclusion:The breakthrough was made by developing a dashboard using forest plots to display the difference in event counts. The visualization of the observed MeSH terms could be replicated for future academic pursuits and applications in other disciplines using the odds of 95% confidence intervals.

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.

Background: The new coronavirus disease 2019 (COVID-19) pandemic is raging worldwide. The administered vaccination has become a significant vehicle against the virus. Three hypotheses were made and required for validation: the number of vaccines administered is related to the country gross domestic product (GDP), vaccines can reduce the fatality rate (FR), and dashboards can present more meaningful information than traditionally static visualizations. Research data were downloaded from the GitHub website. The aims of this study are to verify that the number of vaccination uptakes is related to the country GDP, that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations. Methods: The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website for countries/regions on November 6, 2021. Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions. We applied the Kano model, forest plot, and choropleth map to demonstrate and verify the 3 hypotheses using correlation coefficients (CC) between vaccination and FRs. Dashboards used to display the vaccination effects were on Google Maps. Results: We observed that the higher the GDP, the more vaccines are administered (Association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, as well as the application (app) with dashboard-type choropleth map can be used to show the comparison of vaccination rates for countries/regions using line charts. Conclusion: This research uses the Kano map, forest plot, and choropleth map to verify the 3 hypotheses and provides insights into the vaccination effect against the FR for relevant epidemic studies in the future.