Predicting article citations using data from 100 top-cited publications in the field of Psoriasis Vulgaris and biological agents (PVBA) since 1991: A bibliometric analysis

Abstract

Background: Psoriasis Vulgaris is a chronic inflammatory disease characterized by keratinocyte hyperproliferation. Bibliometric analysis helps determine the most influential article on the topic of "Psoriasis Vulgaris and biological agents (PVBAs)", and what factors affect article citation remain unclear. This study aims (1) to identify the top 100 most cited articles in PVBA (PVBA100 for short) from 1991 to 2020, (2) to visualize dominant entities on one diagram using data in PVBA100, and (3) to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.Methods: The top 100 most cited articles relevant to PVBA (1991PVBA ( -2020 were downloaded by searching the PubMed database. Citation analysis was applied to compare the dominant roles in article types and topic categories using pyramid plots. Social network analysis (SNA) and Sankey diagrams were applied to highlight prominent entities. We examined the MeSH prediction effect on article citations using its correlation coefficients.Results: The most frequent article types and topic categories were research support by institutes (46%) and drug therapy (88%), respectively. The most productive countries were the United States (38%), followed by Germany (13%) and Japan (12%). Most articles were published in Br J Dermatol (13%) and J Invest Dermatol (11%). MeSH terms were evident in the prediction power of the number of article citations (correlation coefficient=0.45, t=4.99). Conclusions:The breakthrough was made by developing one dashboard to display PVBA100. MeSH terms can be used for predicting article citations in PVBA100. These visualizations of PVBA100 could be applied to future academic pursuits and applications in other academic disciplines.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis

“…The Sankey diagram [ 63 , 64 ] was used to identify important information quickly and has recently been applied to bibliometrics, [ 43 , 54 , 65 ] as we have done with Figure 2 in this study. For readers to practice it on their own, a method to draw the Sankey is provided in MS Excel, [ 45 ] in lieu of using the package networkD3 in R language.…”

The 100 most cited articles have fewer citations than other bibliometric articles: A pairwise comparison using a temporal bubble graph

“…The characteristics of 100 top-cited articles are commonly visualized with 3 categories of information on descriptive statistics, research domain, and research achievement (RA). [8,44–49] Some studies [8,44–48,49] have applied citation prediction to predict article citations based on the mean citations of article keywords, but the themes have not been definitely classified and clearly visualized through Equation 4. [9]…”

A leading bibliometric author does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis

“…In an article, each author (or keyword) has an equal weight (=1/L, where L is the number of elements in the article). [26–28]…”

“…In an article, each author (or keyword) has an equal weight (=1/L, where L is the number of elements in the article). [26][27][28] We assigned keywords to each cluster of authors based on the data arrangement within each article by a string using co-word analysis (e.g., [chief author in a cluster, keyword1, keyword2..., keywordL] for each article). A chord diagram [29] was used to display the top major keywords with the highest weighted centrality degree corresponding to each chief author in the network.…”