Dataset Information

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis.

ABSTRACT:

Background

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top 100 most-cited articles, and (2) investigates whether medical subject headings (i.e., MeSH terms) can be used in predicting article citations.

Methods

By searching the PubMed Central^® (PMC) database, the top 100 most-cited abstracts relevant to ADHD since 2014 were downloaded. Citation rank analysis was performed to compare the dominant roles of article types and topic categories using the pyramid plot. Social network analysis (SNA) was performed to highlight prominent entities for providing a quick look at the study result. The authors examined the MeSH prediction effect on article citations using its correlation coefficients (CC).

Results

The most frequent article types and topic categories were research support by institutes (56%) and epidemiology (28%). The most productive countries were the United States (42%), followed by the United Kingdom (13%), Germany (9%), and the Netherlands (9%). Most articles were published in the Journal of the American Academy of Child and Adolescent Psychiatry (15%) and JAMA Psychiatry (9%). MeSH terms were evident in prediction power on the number of article citations (correlation coefficient?=?0.39; t?=?4.1; n?=?94; 6 articles were excluded because they do not have MeSH terms).

Conclusions

The breakthrough was made by developing 1 dashboard to display 100 top-cited articles on ADHD. MeSH terms can be used in predicting article citations on ADHD. These visualizations of the top 100 most-cited articles could be applied to future academic pursuits and other academic disciplines.

SUBMITTER: Lin CH

PROVIDER: S-EPMC7819196 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis.

Lin Chien-Ho CH Chien Tsair-Wei TW Yan Yu-Hua YH

Annals of general psychiatry 20210121 1

<h4>Background</h4>Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top ...[more]

PMID: 33478559

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Project description:BackgroundPublications 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 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.ResultsThe 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).ConclusionThe 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.

Project description:BackgroundThe h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords.MethodsThese 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores.ResultsA coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%).ConclusionA total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.

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Dataset Information

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis.

Background

Methods

Results

Conclusions

Publications

Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis.

Similar Datasets

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