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.

Figure 1.

The improved TBG that can release more information (e.g., burst spot, data trend, increasing type, and heatmap) on the bar graph. TBG = temporal bar graph.

Figure 2.

The IP determined on an ogive curve using the Newton-Raphson Iteration Method(NRIM)[35-38] approach from top to bottom as model-data-fit curves with the number of iterations in process. IP = inflection point.

Figure 3.

An illustrative example of IPs determined on seven output curves (Note. The IPs on the locations of item difficulties form the easy (left) to the hard (right)). IP = inflection point.

Figure 4.

The study flowchart (Note. Trend and prediction using visualizations and weighted MeSH terms to predict the article citations).

Figure 5.

The comparison of three scenarios between the non-AWS and AWS approaches (Note. Only the scenario IV that can make the weighted DC equal counts of publications or citations in articles through a series of adjustments in Equations (1)–(7)). AWS = author-weighted scheme.

Figure 6.

The selection of top 5 topical entities (Note. All entities are analyzed together using SNA and shown on the Alluvial diagram). SNA = social network analysis.

Figure 7.

Citation trend of major topical entities shown on TBG (Note. The first feather of this study is to develop the enhanced TBG used for bubliometrics). TBG = temporal bar graph.

Figure 8.

Citation trend and burst spot for the targeted entity using a line-chart plot (Note. Once the bubble of interest in TBG is clicked, the line chart appears immediately). TBG = temporal bar graph.

Figure 9.

Influential MeSH terms with higher mean citations using SNA to display the IF (i.e., the mean citations based on T100NMJ) on Google Maps. IF = impact factors. MeSH = medical subject headings, T100NMJ = 100 top-cited articles related to NMJ.

Figure 10.

Predicting the number of articles in T100NMJ using the weighted DC of MeSH terms in Equations (1) to (7) (Note. Bubbles are sized and colored by article citations). MeSH = medical subject headings, T100NMJ = 100 top-cited articles related to NMJ.