Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011

Kuo, Yu-Chi; Chien, Tsair‐Wei; Kuo, Shu‐Chun; Yeh, Yu-Tsen; Lin, Jui-Chung John; Fong, Y.

doi:10.1097/md.0000000000022885

Cited by 19 publications

(34 citation statements)

References 32 publications

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“…Social network analysis (SNA) was commonly applied in previous studies, [ 17 , 35 , 36 ] but there was no research, except for the one, [ 37 ] integrated different entities into SNA, such as author-affiliated nations, journals, article types, and topic categories with MeSH terms, and then displayed the dominant ones onto the Sankey diagram [ 20 ] while searching the string of “social network analysis network” and “Sankey diagram” in PubMed database thus far.…”

Section: Discussionmentioning

confidence: 99%

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

Chen

Chien

et al. 2022

Medicine

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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.

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Section: Discussionmentioning

confidence: 99%

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

Chen

Chien

et al. 2022

Medicine

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“…Two studies [ 8 , 9 ] applied the alluvial to the analysis of bibliometric data. Graphs based on the alluvial are appropriate due to the categorical dimensions instead of steps (or years) on the x-axis, as stated in definitions of the alluvial plots.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the classification of subject categories using SNA is objective and unique in comparison with previous studies using manual methods [ 8 ] and document types only in PubMed. [ 9 ] However, the evidence indicates that the classification is meaningful, feasible and worthy of recommendation for future studies in relevant fields (F = 1.19, P = .320).…”

Section: Discussionmentioning

confidence: 99%

“…[ 2 , 7 ] We are interested in using these methods (e.g., programming skills combined with Sankey software) to improve the visualization of data in bibliometric analyses. [ 8 , 9 ] The network characteristics of 100 top-cited articles on attention-deficit/hyperactivity disorder (T100ADHD) were illustrated using a visualization technique (such as Sankey/alluvial diagrams, stacked bar charts, and parallel sets). [ 10 ]…”

Section: Introductionmentioning

confidence: 99%

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Using the alluvial plot to visualize the network characteristics of 100 top-cited articles on attention-deficit/hyperactivity disorder (ADHD) since 2011: Bibliometric analysis

Tsai

Chien

et al. 2022

Medicine

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Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neuro developmental disorder that affects children and adolescents. It is estimated that the prevalence of ADHD is 7.2% throughout the world. There have been a number of articles published in the literature related to ADHD. However, it remains unclear which countries, journals, subject categories, and articles have the greatest influence. The purpose of this study was to display influential entities in 100 top-cited ADHD-related articles (T100ADHD) on an alluvial plot and apply alluvial to better understand the network characteristics of T100ADHD across entities. Methods: Using the PubMed and Web of Science (WoS) databases, T100ADHD data since 2011 were downloaded. The dominant entities were compared using alluvial plots based on citation analysis. Based on medical subject headings (MeSH terms) and research areas extracted from PubMed and WoS, social network analysis (SNA) was performed to classify subject categories. To examine the difference in article citations among subject categories and the predictive power of MeSH terms on article citations in T100ADHD, one-way analysis of variance and regression analysis were used. Results: The top 3 countries (the United States, the United Kingdom, and the Netherlands) accounted for 75% of T100ADHD. The most citations per article were earned by Brazil (=415.33). The overall impact factor (IF = citations per 100) of the T100ADHD series is 188.24. The most cited article was written by Polanczyk et al from Brazil, with 772 citations since 2014. The majority of the articles were published and cited in Biol Psychiatry (13%; IF = 174.15). The SNA was used to categorize 6 subject areas. On the alluvial plots, T100ADHD’s network characteristics were successfully displayed. There was no difference in article citations among subject categories (F = 1.19, P = .320). The most frequently occurring MeSH terms were physiopathology, diagnosis, and epidemiology. A significant correlation was observed between MeSH terms and the number of article citations (F = 25.36; P < .001). Conclusion: Drawing the alluvial plot to display network characteristics in T100ADHD was a breakthrough. Article subject categories can be classified using MeSH terms to predict T100ADHD citations. Bibliometric analyses of 100 top-cited articles can be conducted in the future.

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“…From the radar plot mentioned in the previous section, the top 2 prolific authors with the highest hT-index were selected. We made 3 comparisons in IRA between the 2 authors, including publications on the IBP, [ 39 , 40 ] keyword plus in WoSCC using the Sankey diagram, [ 41 , 42 ] and comparisons of differences in research features on the forest plot. [ 34 , 43 , 44 ]…”

Section: Methodsmentioning

confidence: 99%

Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis

et al. 2022

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Background: The h-index does not take into account the full citation list of a researcher to evaluate individual research achievements (IRAs). As a generalization of the h-index, the hT-index takes all citations into account to evaluate IRAs. Compared to other bibliometric indices, it is unclear whether the hT-index is more closely associated with the h-index. We utilized articles published on hemodialysis and peritoneal dialysis (HD/PD) to validate the hT-index as a measure of the most significant contributions to HD/PD.Methods: Using keywords involving HD/PD in titles, subject areas, and abstracts since 2011, we obtained 7702 abstracts and their associated metadata (e.g., citations, authors, research institutes, countries of origin). In total, 4752 first or corresponding authors with hT-indices >0 were evaluated. To present the author's IRA, the following 4 visualizations were used: radar, Sankey, impact beam plot, and choropleth map to investigate whether the hT-index was more closely associated with the h-index than other indices (e.g., g-/x-indices and author impact factors), whether the United States still dominates the majority of publications concerning PD/HD, and whether there was any difference in research features between 2 prolific authors.Results: In HD/PD articles, we observed that (a) the hT-index was closer to and associated with the h-index; (b1) the United States (37.15), China (34.63), and Japan (28.09) had the highest hT-index; (b2) Sun Yat Sen University (Chian) earned the highest hT-index (=20.02) among research institutes; (c1) the authors with the highest hT-indices (=15.64 and 14.39, respectively) were David W Johnson (Australia) and Andrew Davenport (UK); and (c2) their research focuses on PD and HD, respectively. Conclusion:The hT-index was demonstrated to be appropriate for assessing IRAs along with visualizations. The hT-index is recommended in future bibliometric analyses of IRAs as a complement to the h-index.

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Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011

Abstract: Supplemental Digital Content is available in the text

Cited by 19 publications

References 32 publications

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

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

Using the alluvial plot to visualize the network characteristics of 100 top-cited articles on attention-deficit/hyperactivity disorder (ADHD) since 2011: Bibliometric analysis

Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis

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