Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix

Identifying authorial roles in research: A Kano model-based bibliometric analysis for the Journal of Medicine (Baltimore) 2023

The landscape of research roles within academic journals often remains uncharted territory, with authorial contributions frequently reduced to linear hierarchies (e.g., professor and assistant professor). The Kano model, traditionally used in customer satisfaction research, offers a nuanced framework for identifying the multifaceted roles of authors in scholarly publications. This study utilizes the Kano model to dissect and categorize the roles of authors in the medicine field. To conform to the hypothesis, China is the research leader while the US is the research collaborator, as reflected in the publications of the journal of Medicine (Baltimore) in the year 2023. We conducted a comprehensive bibliometric analysis of all research articles published in the journal of Medicine (Baltimore) in 2023. The Kano model was applied to classify authors into 5 categories reflective of their research roles: followers, leaders, partners, contributors, and collaborators. Data on author publications and co-authorship networks with multi-author rates (MARs) were analyzed to assign Kano categories based on the authorship positions of first and corresponding authors. Descriptive statistics and network analysis tools were used to interpret the data, including radar plots, geographical maps, and Kano diagrams. The analysis covered 1976 articles, uncovering a complex network of author roles that extends beyond the conventional binary distinction of lead and supporting authors (i.e., leading, and following researchers). A research leader in China and a collaborator in the US were conformed to support the hypothesis, based on their publications (1148 vs 51) and MARs (12.20% vs 19.61%). The Kano classification was visually adapted to classify authors (or entities) into 5 categories. The combined choropleth and geographical network maps were illustrated to identify author roles in research briefly. The Kano model serves as an effective tool for uncovering the diverse contributions of authors in medical research. By moving beyond the lead and follower dichotomy, this study highlights the intricate ecosystem of authorial roles, emphasizing the importance of each in advancing knowledge within the field of medicine. Future application of the Kano model could foster a more collaborative and inclusive recognition of contributions across various disciplines.

Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors' IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals

BACKGROUND

The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period.

METHODS

Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm.

RESULTS

Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions.

CONCLUSION

Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD)

BACKGROUND

Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade.

METHODS

We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework.

RESULTS

Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy).

CONCLUSION

The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Identifying China's distinctive academic fields among the top 2% of influential scientists: A bibliometric analysis using Rasch KIDMAP

BACKGROUND

Leading scientists worldwide are recognized by their placement in the top 2% based on their career-spanning contributions, as categorized by the Science-Metrix classification. However, there has been little focus on the unique scientific fields and subfields that separate countries. Although the KIDMAP in the Rasch model has been utilized to depict student performance, its application in identifying distinctive academic areas remains unexplored. Our study uses this model to pinpoint unique research domains specific to countries based on the top 2% author data.

METHODS

We sourced our data from Elsevier career-long author database updated until the end of 2022. This encompassed 168 countries, 22 scientific domains, and 174 subdomains in 2021 and 2022 (with a total of 194,983 and 204,643 researchers, respectively). Our approach was threefold: identifying unique fields, subfields, and researchers. Visualizations included scatter plots, KIDMAP, and the Impact Bam Plot (IBP). China distinctive research areas were identified using the Rasch KIDMAP.

RESULTS

Key insights include the following: The US prevailing dominance in scientific domains in both 2021 and 2022. China distinct contribution in the "Enabling & Strategic Technologies" domain. China notable emphasis on the "Complementary & Alternative Medicine" subfield in 2022. Dr Phillip Low from the Mayo Clinic (US) emerged as a leading figure in the General & Internal Medicine research domain.

CONCLUSIONS

Despite trailing the US in global research achievements, China showcased pronounced expertise in specific scientific areas, such as the "Complementary & Alternative Medicine" subfield in 2022, when compared to China other subfields based on the level of academic performance (-3.09 logits). Future research could benefit from incorporating KIDMAP visuals to gauge other countries' strengths in various research sectors, expanding beyond the China-centric focus in this study.

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis

BACKGROUND

The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis.

METHODS

A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms).

RESULTS

Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%.

CONCLUSIONS

No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

Evaluating cluster analysis techniques in ChatGPT versus R-language with visualizations of author collaborations and keyword cooccurrences on articles in the Journal of Medicine (Baltimore) 2023: Bibliometric analysis

BACKGROUND

Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach.

METHODS

The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals.

RESULTS

The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals.

CONCLUSIONS

This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.

Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis

BACKGROUND

Telerehabilitation offers a novel approach supplementing or replacing traditional physical rehabilitation. While research on telerehabilitation for joint replacement (TJR) has expanded, no study has investigated the top 100 cited articles (T100TJR) using the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model. This study aims to examine the features of T100TJR in TJR through the DDPP approaches.

METHODS

A comprehensive search of the Web of Science Core Collection was conducted to locate all pertinent English-language documents from the database's inception until August 2, 2023. The T100TJR articles were then identified based on citation counts. The DDPP analytics model, along with 7 visualization techniques, was used to analyze metadata elements such as countries, institutions, journals, authors, references, and keywords. An impact timeline view was employed to highlight 2 particularly noteworthy articles.

RESULTS

We analyzed 712 articles and observed a consistent upward trend in publications, culminating in a noticeable peak in 2022. The United States stood out as the primary contributor. A detailed examination of the top 100 articles (T100TJR) revealed the following leading contributors since 2010: the United States (by country), University of Sherbrooke, Canada (by institutions), 2017 (by publication year), and Dr Hawker from Canada (by authors). We delineated 4 major themes within these articles. The theme "replacement" dominated, featuring in 89% of them. There was a strong correlation between the citations an article garnered and its keyword prominence (F = 3030.37; P < .0001). Additionally, 2 particularly high-impact articles were underscored for recommendation.

CONCLUSIONS

Telerehabilitation for TJR has seen rising interest, with the U.S. leading contributions. The study highlighted dominant themes, especially "replacement," in top-cited articles. The significant correlation between article citations and keyword importance indicates the criticality of keyword selection. The research underscores the importance of 2 pivotal articles, recommending them for deeper insights.

Thematic maps with scatter and 4-quadrant plots in R to identity dominant entities on schizophrenia in psychiatry since 2017: Bibliometric analysis

BACKGROUND

Schizophrenia affects more than 21 million people worldwide. There have been a number of articles published in the literature regarding schizophrenia. It is unclear which authors contributed the most to the field of schizophrenia. This study examines which article entities (affiliated countries, institutes, journals, and authors) earn the most research achievements (RAs) and whether keywords in articles are associated with the number of article citations.

METHODS

As of August 25, 2022, 20,606 abstracts published on schizophrenia in psychiatry since 2017 were retrieved from the WoS core collection (WoSCC). RAs were measured using the category, JIF, authorship, and L-index (CJAL) score. The follower-leading cluster algorithm (FLCA) was used to examine clusters of keywords associated with core concepts of research. There were 7 types of visualizations used to report the study results, including Sankey diagrams, choropleth maps, scatter charts, radar plots, and cluster plots. A hypothesis was examined that the mean number of citations for keywords could predict the number of citations for 100 top-cited articles(T100SCHZ).

RESULTS

The results indicate that the US (18861), Kings College London (U.S. (2572), Psychiatry (14603), and Kolanu Nithin (Australia) (9.88) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. The journal of Schizophrenia Res had higher citations (19,017), counts (1681), and mean citations (11.31) in journals. There was a significant correlation between article citations and weighted keywords (F = 1471.74; P < .001).

CONCLUSION

Seven visualizations were presented to report the study results, particularly with thematic maps using scatter and 4-quadrant plots produced in R programming language. We recommend that more future bibliographical studies utilize CAJL scores and thematic maps to report their findings, not restrict themselves solely to schizophrenia in psychiatry as done in this study.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis

BACKGROUND

Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years.

METHODS

Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC).

RESULTS

The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading.

CONCLUSION

By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

Differences in productivity and collaboration patterns on spine-related research between neurosurgeons and orthopedic spine surgeons: Bibliometric analysis

BACKGROUND

Spinal surgeries are commonly performed by neurosurgeons and orthopedic spine surgeons, with many spine-related articles published by them. However, there has been limited research that directly compares their research achievements. This article conducted a comparative analysis of spine-related research achievements between neurosurgeons and orthopedic spine surgeons. This study examines differences in productivity and impact on spine-related research between them using these measures, particularly with a novel clustering algorithm.

METHODS

We gathered 2148 articles written by neurosurgeons and orthopedic spine surgeons from the Web of Science core collections, covering the period from 2013 to 2022. To analyze author collaborations, we employed the follower-leader clustering algorithm (FLCA) and conducted cluster analysis. A 3-part analysis was carried out: cluster analysis of author collaborations; mean citation analysis; and a category, journal, authorship, L-index (CJAL) score based on article category, journal impact factors, authorships, and L-indices. We then utilized R to create visual displays of our findings, including circle bar charts, heatmaps with dendrograms, 4-quadrant radar plots, and forest plots. The mean citations and CJAL scores were compared between neurosurgeons and orthopedic spine surgeons.

RESULTS

When considering first and corresponding authors, orthopedics authors wrote a greater proportion of the articles in the article collections, accounting for 75% (1600 out of 2148). The CJAL score based on the top 10 units each also favored orthopedic spine surgeons, with 71% (3626 out of 6139) of the total score attributed to them. Using the FLCA, we observed that orthopedic spine surgeons tended to have more collaborations across countries. Additionally, while citation per article favored orthopedic spine surgeons with standard mean difference (= -0.66) and 95%CI: -0.76, -0.56, the mean CJAL score in difference (= 0.34) favored neurosurgeons with 95%CI: 0.24 0.44.

CONCLUSION

Orthopedic spine surgeons have a higher number of publications, citations, and CJAL scores in spine research than those in neurosurgeons. Orthopedic spine surgeons tend to have more collaborations and coauthored papers in the field. The study highlights the differences in research productivity and collaboration patterns between the 2 authors in spine research and sheds light on potential contributing factors. The study recommends the use of FLCA for future bibliographical studies.

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis

BACKGROUND

There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics.

METHODS

The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R.

RESULTS

The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US.

CONCLUSION

The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph

BACKGROUND

The appearance of a topic in a document stream is signaled by a burst of activity, with certain features rising sharply in frequency as the topic emerges. Although temporal bar graph (TBG) is frequently applied to present the burst spot in the bibliographical study, none of the research has combined the inflection point (IP) to interpret the burst spot feature. The aims of this study are to improve the traditional TBG and apply the TBG to understand better the evolution of a topic (e.g., publications and citations for a given author).

METHODS

The EISTL model, including entity, indicator, selection of a few vital ones (named attributes) with higher values in quantity (e.g., the citation data of the top 10 entities), TBG and line-chart plots to verify the trend of interest, was proposed to demonstrate the TBG as a whole. The IP locations compared to the median point in data along with the heap map and line-chart trend were identified. The burst strength was computed. A dashboard on Google Maps was designed and launched for bibliometric analysis. Four authors in MDPI (Multidisciplinary Digital Publishing Institute) journals named to be Citation Laureates 2021 were recruited to compare their research achievements shown on the TBG, particularly displaying the burst spots and the recent developments and stages (e.g., increasing, ready to increase, slowdown, or decreasing).

RESULTS

We observed that the highest burst strengths in publication and citations are earned by Barry Halliwell (8.99) and Jean-Pierre Changeux (18.01). The breakthrough of TBG using the EISTL model to display the influence of authors in academics was made with 2 parts of the primary IP point and the trend feature in the data.

CONCLUSION

The dashboard-type TBG shown on Google Maps is unique and innovative and able to provide deeper insights to readers, not merely limited to the publications and citations for a given author as we did in this study.

Analyzing author collaborations by developing a follower-leader clustering algorithm and identifying top co-authoring countries: Cluster analysis

BACKGROUND

This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022.

METHODS

This study extracted article metadata from the Web of Science and used the statistical software R to implement FLCA, enabling efficient and reproducible analysis of ACs and cowords in bibliometrics. To determine the countries that easily coauthored with other countries, the study observed the top 20 countries each year and visualized the results using network charts, heatmaps with dendrograms, and Venn diagrams. The study also used chord diagrams to demonstrate the use of FLCA on ACs and cowords in Medicine (Baltimore).

RESULTS

The study observed 12,793 articles, including 5081, 4418, and 3294 in 2020, 2021, and 2022, respectively. The results showed that the FLCA algorithm can accurately identify clusters in bibliometrics, and the USA, China, South Korea, Japan, and Spain were the top 5 countries that commonly coauthored with others during 2020 and 2022. Furthermore, the study identified China, Sichuan University, and diagnosis as the leading entities in countries, institutes, and keywords based on ACs and cowords, respectively. The study highlights the advantages of using cluster analysis and visual displays to analyze ACs in Medicine (Baltimore) and their potential application to coword analysis.

CONCLUSION

The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.

Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm (FLCA): A bibliometric study

BACKGROUND

Myocarditis can be classified into 2 categories: fulminant myocarditis (FM) and nonfulminant myocarditis. FM is the most severe type, characterized by its acute and explosive nature, posing a sudden and life-threatening risk with a high fatality rate. Limited research has been conducted on FM characteristics using cluster analysis. This study introduces the following-leading clustering algorithm (`) as a unique method and utilizes it to generate a dual map and timeline view of FM themes, aiming to gain a better understanding of FM.

METHODS

The metadata were obtained from the Web of Science (WoS) database using an advanced search strategy based on the topic (TS= (("Fulminant") AND ("Myocarditis"))). The analysis comprised 3 main components: descriptive analytics, which involved identifying the most influential entities using CJAL scores and analyzing publication trends, author collaborations using the FLCA algorithm, and generating a dual map and timeline view of FM themes using the FLCA algorithm. The visualizations included radar plots divided into 4 quadrants, stacked bar and line charts, network charts, chord diagrams, a dual map overlay, and a timeline view.

RESULTS

The findings reveal that the prominent entities in terms of countries, institutes, departments, and authors were the United States, Huazhong University of Science and Technology (China), Cardiology, and Enrico Ammirati from Italy. A dual map, based on the research category, was created to analyze the relationship between citing and cited articles. It showed that articles related to cells and clinical medicine/surgery were frequently cited by articles in the fields of general health/public/nursing and clinical medicine/surgery. Additionally, a visual timeline view was presented on Google Maps, showcasing the themes extracted from the top 100 cited articles. These visualizations were successfully and reliably generated using the FLCA algorithm, offering insights from various perspectives.

CONCLUSION

A new FLCA algorithm was utilized to examine bibliometric data from 1989 to 2022, specifically focusing on FM. The results of this analysis can serve as a valuable guide for researchers, offering insights into the thematic trends and characteristics of FM research development. This, in turn, can facilitate and promote future research endeavors in this field.

Analyzing the number of articles with network meta-analyses using chord diagrams and temporal heatmaps over the past 10 years: Bibliometric analysis

BACKGROUND

Network meta-analyses (NMAs) are statistical techniques used to synthesize data from multiple studies and compare the effectiveness of different interventions for a particular disease or condition. They have gained popularity in recent years as a tool for evidence-based decision making in healthcare. Whether publications in NMAs have an increasing trend is still unclear. This study aimed to investigate the trends in the number of NMA articles over the past 10 years when compared to non-NMA articles.

METHODS

The study utilized data from the Web of Science database, specifically searching for articles containing the term "meta-analysis" published between 2013 and 2022. The analysis examined the annual number of articles, as well as the countries, institutions, departments, and authors associated with the articles and the journals in which they were published. Ten different visualization techniques, including line charts, choropleth maps, chord diagrams, circle packing charts, forest plots, temporal heatmaps, impact beam plots, pyramid plots, 4-quadrant radar plots, and scatter plots, were employed to support the hypothesis that the number of NMA-related articles has increased (or declined) over the past decade when compared to non-NMA articles.

RESULTS

Our findings indicate that there was no difference in mean citations or publication trends between NMA and non-NMA; the United States, McMaster University (Canada), medical schools, Dan Jackson from the United Kingdom, and the Journal of Medicine (Baltimore) were among the leading entities; NMA ranked highest on the coword analysis, followed by heterogeneity, quality, and protocol, with weighted centrality degrees of 32.51, 30.84, 29.43, and 24.26, respectively; and the number of NMA-related articles had increased prior to 2020 but experienced a decline in the past 3 years, potentially due to being overshadowed by the intense academic focus on COVID-19.

CONCLUSION

It is evident that the number of NMA articles increased rapidly between 2013 and 2019 before leveling off in the years following. For researchers, policymakers, and healthcare professionals who are interested in evidence-based decision making, the visualizations used in this study may be useful.

The 95% control lines on both confirmed cases and days of infection with COVID-19 were applied to compare the impact on public health between 2020 and 2021 using the hT-index

BACKGROUND

COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. The number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. Scatter plots are frequently used to interpret the impact in relation to confirmed cases. However, the 95% confidence intervals are rarely given to the scatter plot. The objective of this study was to; Develop 95% control lines on daily confirmed cases and infected days for countries/regions in COVID-19 (DCCIDC) and; Examine their impacts on public health (IPH) using the hT-index.

METHODS

All relevant COVID-19 data were downloaded from GitHub. The hT-index, taking all DCCIDCs into account, was applied to measure the IPHs for counties/regions. The 95% control lines were proposed to highlight the outliers of entities in COVID-19. The hT-based IPHs were compared among counties/regions between 2020 and 2021 using the choropleth map and the forest plot. The features of the hT-index were explained using the line chart and the box plot.

RESULTS

The top 2 countries measured by hT-based IPHs were India and Brazil in 2020 and 2021. The outliers beyond the 95% confidence intervals were Hubei (China), with a lower hT-index favoring 2021 ( = 6.4 in 2021 vs 15.55 in 2020) and higher hT indices favoring 2021 in Thailand (28.34 vs 14,77) and Vietnam (27.05 vs 10.88). Only 3 continents of Africa, Asia, and Europe had statistically and significantly fewer DCCIDCs (denoted by the hT-index) in 2021. The hT-index generalizes the h-index and overcomes the disadvantage without taking all elements (e.g., DCCIDCs) into account in features.

CONCLUSIONS

The scatter plot combined with the 95% control lines was applied to compare the IPHs hit by COVID-19 and suggested for use with the hT-index in future studies, not limited to the field of public health as we did in this research.

The ascendancy of research in acronyms related to COVID-19 displayed on a growth-share matrix (GSM): Bibliometric analysis

BACKGROUND

The acronym COVID, which stands for coronavirus disease, has become one of the most infamous acronyms in the world since 2020. An analysis of acronyms in health and medical journals has previously found that acronyms have become more common in titles and abstracts over time (e.g., DNA and human immunodeficiency virus are the most common acronyms). However, the trends in acronyms related to COVID remain unclear. It is necessary to verify whether the dramatic rise in COVID-related research can be observed by visualizations. The purpose of this study was to display the acronym trends in comparison through the use of temporal graphs and to verify that the COVID acronym has a significant edge over the other 2 in terms of research dominance.

METHODS

An analysis of the 30 most frequently used acronyms related to COVID in PubMed since 1950 was carried out using 4 graphs to conduct this bibliometric analysis, including line charts, temporal bar graphs (TBGs), temporal heatmaps (THM), and growth-share matrices (GSM). The absolute advantage coefficient (AAC) was used to measure the dominance strength for COVID acronym since 2020. COVID's AAC trend was expected to decline over time.

RESULTS

This study found that COVID, DNA, and human immunodeficiency virus have been the most frequently observed research acronyms since 2020, followed by computed tomography and World Health Organization; although there is no ideal method for displaying acronym trends over time, researchers can utilize the GSM to complement traditional line charts, TBGs, and THMs, as shown in this study; and COVID has a significant edge over the other 2 in terms of research dominance by ACC (≥0.67), but COVID's AAC trend has declined (e.g., AACs 0.83, 0.80, and 0.69) since 2020.

CONCLUSIONS

It is recommended that the GSM complement traditional line charts, TBGs, and THMs in trend analysis, rather than being restricted to acronyms in future research. This research provides readers with the AAC to understand how research dominates its counterparts, which will be useful for future bibliometric analyses.

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

BACKGROUND

There have been nearly 200 thousand meta-analysis articles indexed by web of science (WoS) since 2013. To date, a bibliometric analysis of leading authors of meta-analyses that contribute to the field has not been conducted. Analyzing trend patterns in article citations and comparing individual research achievements (IRAs) are required following the extraction of meta-analysis articles. Using trend analysis, this study aims to verify the hypotheses that; The leading author has a dominant research achievement and; Recent articles that deserve worth reading can be identified.

METHODS

In the WoS collection, we identified the top 20 authors with the most articles related to meta-analysis. Using coword analysis, 2882 articles were collected to cluster author collaborations and identify the top 3 authors with the highest weighted centrality degrees. Based on the CJAL (category, journal raking by impact factor, authorship, and L-index on article citation) score and absolute advantage coefficient (AAC), we compared the IRAs and identified the author who dominated the field significantly beyond the next 2 authors. In WoS collection, coword analysis was used to highlight the characteristics of research domains for the top authors contributing to meta-analyses. The selection of articles that deserve reading is based on a temporal heatmap.

RESULTS

The top 2 authors were Young-Ho Lee (South Korea), Patompong Ungprasert (U.S.), and Brendon Stubbs (US) with CJAL scores of 240.71, 230.99, and 240.71, respectively. Based on the weak dominance coefficient (AAC = 0.49 < 0.50), it is evident that the leading meta-analysis author does not possess a significant dominant position over the next 2 leading authors in IRAs. Coword analysis was used to illustrate the characteristics of the 3 authors research domains. The 3 articles worth reading were selected based on a trend analysis of the last 4 years using the temporal heatmap.

CONCLUSION

A coword analysis of meta-analysis studies identified 3 leading authors. There was no evidence that 1 author possessed a dominant position due to the lower AAC (=0.49 < 0.50) for the leading author. As we have demonstrated in this study, the CJAL score and the AAC can be applied to many bibliographical studies in the future.

Analysis of citation trends to identify articles on delirium worth reading using DDPP model with temporal heatmaps (THM): A bibliometric analysis

BACKGROUND

Delirium is one of the most common geriatric syndromes in older patients, accounting for 25% of hospitalized older patients, 31 to 35% of patients in the intensive care unit, and 8% to 17% of older patients in the emergency department (ED). A number of articles have been published in the literature regarding delirium. However, it is unclear about article citations evolving in the field. This study proposed a temporal heatmap (THM) that can be applied to all bibliographical studies for a better understanding of cited articles worth reading.

METHODS

As of November 25, 2022, 11,668 abstracts published on delirium since 2013 were retrieved from the Web of Science core collection. Research achievements were measured using the CJAL score. Social network analysis was applied to examine clusters of keywords associated with core concepts of research. A THM was proposed to detect articles worth reading based on recent citations that are increasing. The 100 top-cited articles related to delirium were displayed on an impact beam plot (IBP).

RESULTS

The results indicate that the US (12474), Vanderbilt University (US) (634), Anesthesiology (2168), and Alessandro Morandi (Italy) (116) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. Articles worthy of reading were highlighted on a THM and an IBP when an increasing trend of citations over the last 4 years was observed.

CONCLUSION

The THM and IBP were proposed to highlight articles worth reading, and we recommend that more future bibliographical studies utilize the 2 visualizations and not restrict them solely to delirium-related articles in the future.

The model of descriptive, diagnostic, predictive, and prescriptive analytics on 100 top-cited articles of nasopharyngeal carcinoma from 2013 to 2022: Bibliometric analysis

BACKGROUND

Nasopharyngeal carcinomas (NPCs) are prevalent in southeast Asia. There is a need to systematically review the current trend and status of NPC research. However, most bibliometric analyses have tended to focus on descriptive and diagnostic analytics rather than predictive and prescriptive analyses. Thus, it is necessary to use the model of the 4 (called the descriptive, diagnostic, predictive, and prescriptive analytics [DDPP]) to derive insights from the data. This study aimed to apply the DDPP model to classify article themes and illustrate the characteristics of NPCs; compare NPC researcher achievements across countries, institutes, departments, and authors; determine whether the mean citations of keywords can be used to predict article citations; and highlight articles that are worthy of reading.

METHODS

The Web of Science Core Collection was searched for 100 top-cited articles and reviews related to NPCs published between 2013 and 2022. As part of Microsoft Office Excel 2019, Visual Basic for Applications was used to illustrate the number of publications and scientific productivity of authors over time and to generate network/temporal heatmaps, chord/Sankey diagrams, radar/impact beam plots, and scatter/pyramid charts about collaborations among countries. The DDPP model identifies institutions, authors, and hotspots of NPC research. The category, journal, authorship, and L-index (CJAL) score was applied to evaluate individual research achievements.

RESULTS

A total of 10,564 publications were extracted from Web of Science Core Collection and screened for 100 top-cited articles and reviews related to NPCs. Despite having the highest number of publications (36%), China lags slightly behind the US in CJAL scores. CJAL was higher at Sun Yat-Sen University, Radiat Oncol department, and author Jun Ma from China. The number of article citations was significantly correlated with the number of weighted keywords (F = 1791.17; P < .0001). Six articles with significantly increasing citations over the last 4 years were recommended.

CONCLUSION

This bibliometric study utilizes the DDPP model to analyze the scientific progress of NPC over the past decade. The whole genome is a hot topic that may prove to be a promising research area in the future. A temporal heatmap may serve as a tool for providing readers with articles that are worth reading, which could lead to additional research in bibliometrics.

Using temporal heatmaps to identify worthwhile articles on immune checkpoint blockade for melanoma (ICBM) in Mainland China, Hong Kong, and Taiwan since 2000: A bibliometric analysis

BACKGROUND

Melanoma is a life-threatening form of skin cancer. Due to its remarkable effectiveness, the immune checkpoint blockade is widely used to treat melanoma (ICBM). No research has been conducted on ICBM for identifying the most readable articles. A bibliometric analysis of 100 top-cited ICBM (T100ICBM) in recent decades is required to highlight articles worth reading.

METHODS

Based on the Web of Science Core Collection, we summarized the articles on ICBM published in each year from 2000 to 2022, with first authors from Mainland China, Hong Kong, and Taiwan (CHT). Using the CJAL score, data extraction and visualization of the distribution of ICBM publications were conducted on 2718, and 100 top-cited articles, respectively. We used the temporal heatmap to identify the most readable articles. Four descriptive, diagnostic, predictive, and prescriptive analytics (called DDPP model) were applied to describe the features of T100ICBM articles. The absolute advantage coefficient was used to determine the dominance extent of the most influential region, institute, department, and author.

RESULTS

A total of 2718 publications was included after removing first or corresponding authors who were not affiliated with CHT. Publications by year showed a sharp increase from 2014 onward and either peaked in 2022 or have not yet peaked. It was evident that there was a large difference between the number of publications in provinces/metropolitan cities/regions on CHT. Beijing, Sichuan University, Oncology, and Guo Jun from Beijing are the most prolific and influential region, institute, department, and author. When comparing research achievements to the next productive authors based on the CJAL score, only Dr Jun has a medium effect of dominance (=0.60). On the basis of their consecutive growth in citations over the past 4 years, 20 T100ICBM articles were recommended for readers.

CONCLUSION

The field of ICBM is growing rapidly, and Beijing and Sichuan University are taking the lead in CHT. Furthermore, the study provides references for worth-reading articles using the temporal heatmap. Future research hot spots may focus on these 4 themes of immunotherapy, melanoma, metastatic melanoma, regulatory T cells, cells, and activation, which may pave the way for additional study.

Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review

Aim

The use of information-based solutions such as dashboards is on the rise for taking fact-based actions against the COVID-19 crisis. This scoping review aimed to comprehensively investigate COVID-19 dashboards from different technical perspectives.

Subject and methods

Three main bibliographic databases, PubMed, Web of Science, and Scopus, were searched on 28 August 2021 to retrieve relevant studies. Arksey and O'Malley's (Int J Soc Res Methodol 8(1):19-32, 2005) methodological framework and the enhanced version of this methodology developed by Levac et al. (Implement Sci 5(1):1-9, 2010) were adopted for conducting this review.

Results

In total, 26 articles were included. The COVID-19 dashboards mainly focused on the infected (n = 25), deceased (n = 17), and recovered cases (n = 13), as well as the performed test (n = 10). Most of the dashboards were interactive, with public accessibility targeting various user groups. While some dashboards were both informative and supportive (38%), most were mainly informative (92%). The dashboard data were generally analyzed using simple techniques (58%) and delivered through web-based applications (88%).

Conclusion

Dashboards can help immediately manage, analyze, and summarize a huge amount of information about a COVID-19 outbreak. The findings revealed that the developed COVID-19 dashboards share more or less analogous characteristics that could lay the groundwork for designing and developing dashboards for any other pandemic.

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

BACKGROUND

A total of 22,367 bibliometric articles have been indexed by Web of Science (WoS). The most significant contribution to the field has not yet been identified through bibliometric analysis. A comparison of individual research achievements (IRAs) and trend analysis of article citations are required after extracting bibliometric articles. The study aimed to confirm whether the leading author has a dominant RA and which articles are worth reading for readers using trend analysis.

METHODS

We identified authors with at least 100 articles related to bibliometrics in the WoS core collection. A total of 399 articles were collected to cluster author collaborations. Co-word analysis and chord diagrams were used to match chief authors in clusters with Keywords Plus in WoS core collection. The category, journal impact factor, authorship, and L-index (CJAL) score and the absolute advantage coefficient (AAC) were used to compare IRAs and identify the leading author who dominated the field significantly beyond the next 2 authors. In addition to network charts and chord diagrams, 4 visualizations were used to report study results, including a Sankey diagram, a dot plot, a temporal trend graph, and a radar plot. The temporal bubble graph was used to select articles that deserve to be read.

RESULTS

The top 3 authors were Lutz Bornmann, Yuh-Shan Ho, and Giovanni Abramo, with CJAL scores of 176.22, 176.02, and 112.06, respectively, from Germany, Italy, and Taiwan. Based on the weak dominance coefficient (AAC = 0.20 < 0.70), it is evident that the leading bibliometric author has no such significant power beyond the next 2 leading authors in IRAs. A trend analysis of the last 4 years was used to illustrate the 2 articles that deserve to be read.

CONCLUSION

Three leading authors were identified through a co-word analysis of bibliometrics. There was no evidence of an author who possessed a dominant position due to a lower AAC on the leading author. The CJAL score and the AAC can be applied to many bibliographical studies in the future rather than being limited to bibliometric studies that evaluate the leading authors in a field, as we did in this study.

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

BACKGROUND

We selected authors from mainland China, Hong Kong, and Taiwan (CHT) to examine citation trends on articles and keywords. The existence of suitable temporal bar graphs (TBGs) for displaying citation trends is unknown. It is necessary to enhance the traditional TBGs to provide readers with more information about the citation trend. The purpose of this study was to propose an advanced TBG that can be applied to understand the most worth-reading articles by ophthalmology authors in the CHT.

METHODS

Using the search engine of the Web of Science core collection, we conducted bibliometric analyses to examine the article citation trends of ophthalmology authors in CHT since 2013. A total of 6695 metadata was collected from articles and review articles. Using radar plots, the Y-index, and the combining the Y-index with the CJAL scores (CJAL) scores, we could determine the dominance of publications by year, region, institute, journal, department, and author. A choropleth map, a dot plot, and a 4-quadrant radar plot were used to visualize the results. A TBG was designed and provided for readers to display citation trends on articles and keywords.

RESULTS

We found that the majority of publications were published in 2017 (2275), Shanghai city (935), Sun Yat-Sen University (China) (689), the international journal Ophthalmology (1399), the Department of Ophthalmology (3035), and the author Peizeng Yang (Chongqing) (65); the highest CAJL scores were also from Guangdong (2767.22), Sun Yat-Sen University (China) (2147.35), and the Ophthalmology Department (7130.96); the author Peizeng Yang (Chongqing) (170.16) had the highest CAJL; and the enhanced TBG features maximum counts and recent growth trends that are not included in traditional TBGs.

CONCLUSION

Using the Y-index and the CJAL score compared with research achievements of ophthalmology authors in CHT, a 4-quadrant radar plot was provided. The enhanced TBGs and the CJAL scores are recommended for future bibliographical studies.

The use of the time-to-event index (Tevent) to compare the negative impact of COVID-19 on public health among continents/regions in 2020 and 2021: An observational study

BACKGROUND

During the COVID-19 pandemic, how to measure the negative impact caused by COVID-19 on public health (ImpactCOV) is an important issue. However, few studies have applied the bibliometric index, taking both infected days (quantity) and impact (damage) into account for evaluating ImpactCOV thus far. This study aims to verify the proposed the time-to-event index (Tevent) that is viable and applicable in comparison with 11 other indicators, apply the Tevent to compare the ImpactCOVs among groups in continents/countries in 2020 and 2021, and develop an online algorithm to compute the Tevent-index and draw the survival analysis.

METHODS

We downloaded COVID-19 outbreak data of daily confirmed cases (DCCs) for all countries/regions. The Tevent-index was computed for each country and region. The impactCOVs among continents/countries were compared using the Tevemt indices for groups in 2020 and 2021. Three visualizations (i.e., choropleth maps, forest plot, and time-to-event, a.k.a. survival analysis) were performed. Online algorithms of Tevent as a composite score to denote the ImpactCOV and comparisons of Tevents for groups on Google Maps were programmed.

RESULTS

We observed that the top 3 countries affected by COVID-19 in 2020 and 2021 were (India, Brazil, Russia) and (Brazil, India, and the UK), respectively; statistically significant differences in ImpactCOV were found among continents; and an online time-event analysis showed Hubei Province (China) with a Tevent of 100.88 and 6.93, respectively, in 2020 and 2021.

CONCLUSION

The Tevent-index is viable and applicable to evaluate ImpactCOV. The time-to-event analysis as a branch of statistics for analyzing the expected duration of time until 1 event occurs is recommended to compare the difference in Tevent between groups in future research, not merely limited to ImpactCOV.

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

BACKGROUND

A consensus exists that the first author and corresponding author make the most contribution to the publication of an article. The Y-index has been proposed to assess the scientific achievements of authors, institutions, and countries/regions (AIC/R for short) based on the number of first-author publications (FPs) and corresponding-author publications (RPs). Nonetheless, the Y-index is defined in terms of count and radian (represented by j and h) instead of using the relative radius and angle degree to simplify understanding. In the literature, a method for drawing radar diagrams online with the Y-index is also lacking. This study was conducted to enhance the Y-index with an additional relative radius denoted by k and the angle degree represented by h* (named Yk-index), include easy-to-use features (e.g., copying and pasting) for the delivery of the online Radar-Yk, and identify which one of AIC/R contributed the most to a scientific journal.

METHODS

From the Web of Science (WoS) database, we downloaded 9498 abstracts of articles published in the journal of Medicine (Baltimore) in 2020 and 2021. Three visual representations were used, including a Sankey diagram, a choropleth map, and a radar diagram, to identify the characteristics of contributions by AIC/R to Medicine (Baltimore) using the Yk-index (j, k, h*). A demonstration of Rada-Yk with easy-to-use features was given using the copy-and-paste technique.

RESULTS

We found that Qiu Chen (China), Sichuan University (China), China, and South Korea (based on regions, e.g., provinces/metropolitan areas in China) were the most productive AIC/R, with their Yk equal to 27,715, 12415.1, and 2045, respectively; a total of 85.6% of the published articles in Medicine (Baltimore) came from the 3 countries (China, South Korea, and Japan); and this method of drawing the Radar-Yk online was provided and successfully demonstrated.

CONCLUSION

A breakthrough was achieved by developing the online Radar-Yk to show the most contributions to Medicine (Baltimore). Visualization of Radar-Yk could be replicated for future academic research and applications on other topics in future bibliographical studies.

Using Sankey diagrams to explore the trend of article citations in the field of bladder cancer: Research achievements in China higher than those in the United States

BACKGROUND

Urology authors are required to evaluate research achievements (RAs) in the field of bladder cancer (BC). However, no such bibliometric indices were appropriately applied to quantify the contributions to BC in research. In this study, we examined 3 questions: whether RAs in China are higher than those in the United States, how the Sankey-based temporal bar graph (STBG) may be applied to the analysis of the trend of article citations in the BC field, and what subthemes were reflected in China's and the United States' proportional counts in BC articles.

METHODS

Using the PubMed search engine to download data, we conducted citation analyses of BC articles authored by urology scholars since 2012. A total of 9885 articles were collected and analyzed using the relative citations ratios (RCRs) and the STBG. The 3 research goals were verified using the RCRs, the STBG, and medical subject headings (MesH terms). The choropleth map and the forest plot were used to 1 highlight the geographical distributions of publications and RCRs for countries/regions and 2 compare the differences in themes (denoted by major MeSH terms on proportional counts using social network analysis to cluster topics) between China and the United States.

RESULTS

There was a significant rise over the years in RCRs within the 9885 BC articles. We found that the RCRs in China were substantially higher than those in the United States since 2017, the STBG successfully explored the RCR trend of BC articles and was easier and simpler than the traditional line charts, area plots, and TBGs, and the subtheme of genetics in China has a significantly higher proportion of articles than the United States. The most productive and influential countries/regions (denoted by RCRs) were {Japan, Germany, and Italy} and {Japan, Germany, New York}, respectively, when the US states and provinces/metropolitan cities/areas in China were separately compared to other countries/regions.

CONCLUSIONS

With an overall increase in publications and RCRs on BC articles, research contributions assessed by the RCRs and visualized by the STBGs are suggested for use in future bibliographical studies.

Using sentiment analysis to identify similarities and differences in research topics and medical subject headings (MeSH terms) between Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) in 2020: A bibliometric study

Background:

Little systematic information has been collected about the nature and types of articles published in 2 journals by identifying the latent topics and analyzing the extracted research themes and sentiments using text mining and machine learning within the 2020 time frame. The goals of this study were to conduct a content analysis of articles published in 2 journals, describe the research type, identify possible gaps, and propose future agendas for readers.

Methods:

We downloaded 5610 abstracts in the journals of Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) from the PubMed library in 2020. Sentiment analysis (ie, opinion mining using a natural language processing technique) was performed to determine whether the article abstract was positive or negative toward sentiment to help readers capture article characteristics from journals. Cluster analysis was used to identify article topics based on medical subject headings (MeSH terms) using social network analysis (SNA). Forest plots were applied to distinguish the similarities and differences in article mood and MeSH terms between these 2 journals. The Q statistic and I 2 index were used to evaluate the difference in proportions of MeSH terms in journals.

Results:

The comparison of research topics between the 2 journals using the 737 cited articles was made and found that most authors are from mainland China and Taiwan in Medicine and JFMA, respectively, similarity is supported by observing the abstract mood (Q = 8.3, I 2 = 0, P = .68; Z = 0.46, P = .65), 2 journals are in a common cluster (named latent topic of patient and treatment) using SNA, and difference in overall effect was found by the odds ratios of MeSH terms (Q = 185.5 I 2 = 89.8, P < .001; Z = 5.93, P < .001) and a greater proportion of COVID-19 articles in JFMA.

Conclusions:

SNA and forest plots were provided to readers with deep insight into the relationships between journals in research topics using MeSH terms. The results of this research provide readers with a concept diagram for future submissions to a given journal.

Highlights

The main approaches frequently used in Meta-analysis for drawing forest plots contributed to the following:

Suggestions to the article: demonstrating the ascendancy of COVID-19 research using acronyms

The article published on 16 May 2021 is interesting and impressive, particularly on the Figure displaying several acronyms in trend. Although the most popular eight acronyms in 2019 and 2020 are individually highlighted and labeled, how to determine the points in 2019 and 2020 is required for classifications. The analysis for the evolution of keywords is common and necessary in the bibliographic study. None of the studies addressed the determination of the bursting point for a given keyword over the years. We aim to illustrate the way to determine the inflection point on a given ogive curve and apply the temporal bar graph (TBG) to interpret the trend of a specific keyword (or acronym). The prediction model is based on item response theory, commonly used in educational and psychometric fields. The eight acronyms presented in the previous study were demonstrated using the TBG. We found that the TBG includes more valuable information than the traditional trend charts. The inflection point denoted the topic burst indicates the turning point suddenly from increasing to decreasing. The TBG combined with the inflection point to represent the trend of a given keyword can make the data in trend easier and clearer to understand than any graph used in ever before bibliometric analyses.

Visualizing the features of inflection point shown on a temporal bar graph using the data of COVID-19 pandemic

BACKGROUND

Exponential-like infection growth leading to peaks (denoted by inflection points [IP] or turning points) is usually the hallmark of infectious disease outbreaks, including coronaviruses. To determine the IPs of the novel coronavirus (COVID-19), we applied the item response theory model to detect phase transitions for each country/region and characterize the IP feature on the temporal bar graph (TBG).

METHODS

The IP (using the item difficulty parameter to locate) was verified by the differential equation in calculus and interpreted by the TBG with 2 virtual and real empirical data (i.e., from Collatz conjecture and COVID-19 pandemic in 2020). Comparisons of IPs, R2, and burst strength [BS = ln() denoted by the infection number at IP(Nip) and the item slope parameter(a) in item response theory were made for countries/regions and continents on the choropleth map and the forest plot.

RESULTS

We found that the evolution of COVID-19 on the TBG makes the data clear and easy to understand, the shorter IP (=53.9) was in China and the longest (=247.3) was in Europe, and the highest R2 (as the variance explained by the model) was in the US, with a mean R2 of 0.98. We successfully estimated the IPs for countries/regions on COVID-19 in 2020 and presented them on the TBG.

CONCLUSION

Temporal visualization is recommended for researchers in future relevant studies (e.g., the evolution of keywords in a specific discipline) and is not merely limited to the IP search in COVID-19 pandemics as we did in this study.

Questions to the article: demonstrating the ascendancy of COVID-19 research using acronyms

The article published on 16 May 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as the presentations in Table 1 and Fig. 1 that should be improved further for providing more valuable information to readers. After viewing Table 1, measuring the strength of quantity (= 0.84) referred to the next two counterparts for the top one acronym (e.g., COVID) is demonstrated using the absolute advantage coefficient (AAC). Similarly, Traditional line charts on top-eight acronyms provide us with messages, including (i) DNA and RNA are popular over three decades; (ii) CT, MRI, HIV, SARS, and CoV start in 1972, 1985, 1986, 2003, and 2003, respectively; (iii) the number of COVID substantially surpasses over other seven acronyms in 2020 though the seven acronyms are almost equal in quantity in 2020. We are interested in producing similar Table 1 and Fig. 1 with a video MP4 provided to readers who can click on the link to manipulate the scenarios on their own. We found that the AAC and the traditional line charts on a dashboard make data clear for a better understanding of demonstrating the ascendancy of COVID-19 research using acronyms. The line charts are easily examined on Google Maps.