Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis

Abstract

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.

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.…”

Thematic analysis of articles on artificial intelligence with spine trauma, vertebral metastasis, and osteoporosis using chord diagrams: A systematic review and meta-analysis

“…In this study, the WCD is fixed to 1.0 regardless of the number of keywords in an article via equations 6 to 8. [ 40 ]…”

Citation trends in ophthalmology articles and keywords in mainland China, Hong Kong, and Taiwan since 2013 using temporal bar graphs (TBGs): Bibliometric analysis

“…A trend analysis of article citations is also commonly used in bibliographic studies to describe the development of topical entities over time. [18,19] Thus, a good bibliographic study provides a chronological view of the field (e.g., how the number of studies has evolved, how the topic has evolved, and how the outlet has changed). A TBG, [17,[20][21][22] for interpreting the evolution of article entities, is one method of interpreting the influence of the most influential entities.…”