Dataset Information

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis.

ABSTRACT: BACKGROUND:Publications 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. RESULTS:The 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). CONCLUSION:The 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.

SUBMITTER: Kuo YC

PROVIDER: S-EPMC7598835 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis.

Kuo Yu-Chi YC Chien Tsair-Wei TW Kuo Shu-Chun SC Yeh Yu-Tsen YT Lin Jui-Chung John JJ Fong Yao Y

Medicine 20201001 44

<h4>Background</h4>Publications 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 downloade ...[more]

PMID: 33126338

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Project description:BackgroundAttention-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.MethodsBy 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).ResultsThe 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).ConclusionsThe 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.

Project description:Importance:Citation analysis is a bibliometric method that uses citation rates to evaluate research performance. This type of analysis can identify the articles that have shaped the modern history of obstetrics and gynecology (OBGYN). Objectives:To identify and characterize top-cited OBGYN articles in the Institute for Scientific Information Web of Science's Science Citation Index Expanded and to compare top-cited OBGYN articles published in specialty OBGYN journals with those published in nonspecialty journals. Design, Setting, and Participants:Cross-sectional bibliometric analysis of top-cited articles that were indexed in the Science Citation Index Expanded from 1980 to 2018. The Science Citation Index Expanded was queried using search terms from the American Board of Obstetrics and Gynecology's 2018 certifying examination topics list. The top 100 articles from all journals and the top 100 articles from OBGYN journals were evaluated for specific characteristics. Data were analyzed in March 2019. Main Outcomes and Measures:The articles were characterized by citation number, publication year, topic, study design, and authorship. After excluding articles that featured on both lists, top-cited articles were compared. Results:The query identified 3?767?874 articles, of which 278?846 (7.4%) were published in OBGYN journals. The top-cited article was published by Rossouw and colleagues in JAMA (2002). Top-cited articles published in nonspecialty journals were more frequently cited than those in OBGYN journals (median [interquartile range], 1738 [1490-2077] citations vs 666 [580-843] citations, respectively; P?<?.001) and were more likely to be randomized trials (25.0% vs 2.2%, respectively; difference, 22.8%; 95% CI, 13.5%-32.2%; P?<?.001). Whereas articles from nonspecialty journals focused on broad topics like osteoporosis, articles from OBGYN journal focused on topics like preeclampsia and endometriosis. Conclusions and Relevance:This study found substantial differences between top-cited OBGYN articles published in nonspecialty vs OBGYN journals. These differences may reflect the different goals of the journals, which work together to ensure optimal dissemination of impactful articles.

Dataset Information

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis.

Publications

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis.

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