Predicting the number of article citations on the topic of pemphigus vulgaris with the 100 top-cited articles since 2011: A protocol for systematic review and meta-analysis. Issue 31 (6th August 2021)

• Record Type: Journal Article
• Authors: Yang, Ting-Ya; Chen, Chieh-Hsun; Chien, Tsair-Wei; Lai, Feng-Jie
• Other Names: Sayil. Cigdem section editor.
• Is Part Of: Medicine. Volume 100:Issue 31(2021)
• Publication Date: 2021-08-06
• Subjects: bibliometric -- citation analysis -- correlation coefficient -- medical subject heading -- pemphigus vulgaris -- social network analysis; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000026806
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: Background: Pemphigus vulgaris (PV) is a rare autoimmune blistering disease characterized by intraepithelial and mucocutaneous blister formation and erosion. Numerous articles related to PV have been published. However, which articles have a tremendous influence is still unknown, and factors affecting article citation numbers remain unclear. We aimed to visualize the prominent entities using the top 100 most-cited articles on the topic of PV (T100PV), and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations. Methods: By searching the PubMed Central (PMC) database, the T100PV abstracts since 2011 were downloaded. Citation analysis was performed to compare the dominant entities in article topics, authors, and research institutes using social network analysis (SNA) and Kano diagrams. We examined the MeSH prediction power against article citations using correlation coefficients (CCs). Results: The most cited article (125 times) was authored by Ellebrecht from the University of Pennsylvania in the US. The most productive countries were Germany (28%) and the US (25%). Most articles were published in J Invest Dermatol (16%) and Br J Dermatol (10%). Kasperkiewicz (Germany) and the Normandie University (France) were the most cited authors and research institutes, respectively. The most frequently occurred MeSH terms were administration and dosage, immunology, and metabolism. MeSH terms were evident in the prediction power on the number of article citations ( F = 19.77; P < .001). Conclusion: A breakthrough was achieved by developing dashboards to display the T100PV. MeSH terms can be used to predict the T100PV citations. These T100PV visualizations can be applied in future studies. Abstract : Supplemental Digital Content is available in the text