Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis

Trends and hotspots related to traditional and modern approaches on acupuncture for stroke: A bibliometric and visualization analysis

BACKGROUND

Acupuncture role in stroke treatment and post-stroke rehabilitation has garnered significant attention. However, there is a noticeable gap in bibliometric studies on this topic. Additionally, the precision and comprehensive methodology of cluster analysis remain underexplored. This research sought to introduce an innovative cluster analysis technique (called follower-leading clustering algorithm, FLCA) to evaluate global publications and trends related to acupuncture for stroke in the recent decade.

METHODS

Publications pertaining to acupuncture for stroke from 2013 to 2022 were sourced from the Web of Science Core Collection. For the assessment of publication attributes-including contributing countries/regions (e.g., US states, provinces, and major cities in China) in comparison to others, institutions, departments, authors, journals, and keywords-we employed bibliometric visualization tools combined with the FLCA algorithm. The analysis findings, inclusive of present research status, prospective trends, and 3 influential articles, were presented through bibliometrics with visualizations.

RESULTS

We identified 1050 publications from 92 countries/regions. An initial gradual rise in publication numbers was observed until 2019, marking a pivotal juncture. Prominent contributors in research, based on criteria such as regions, institutions, departments, and authors, were Beijing (China), Beijing Univ Chinese Med (China), the Department of Rehabilitation Medicine, and Lidian Chen (Fujian). The journal "Evid.-based Complement Altern" emerged as the most productive. The FLCA algorithm was effectively employed for co-word and author collaboration analyses. Furthermore, we detail the prevailing research status, anticipated trends, and 3 standout articles via bibliometrics.

CONCLUSION

Acupuncture for stroke presents a vast research avenue. It is imperative for scholars from various global regions and institutions to transcend academic boundaries to foster dialogue and cooperation. For forthcoming bibliometric investigations, the application of the FLCA algorithm for cluster analysis is advocated.

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis

BACKGROUND

The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis.

METHODS

The web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail.

RESULTS

Between 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years.

CONCLUSIONS

This study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.

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.

Using Rasch KIDMAP to identify whether China dominates the research area of computer science (CS) based on the specialization index of article citations: Bibliometric analysis

BACKGROUND

Scientific comparative advantage is measured by using a specialization index (SI) of article citations. The profile data have been published in the literature. However, no such research has been conducted to determine which countries dominate the field of computer science (CS) (subject category [SC]) using the SI. A KIDMAP in the Rasch model has been applied to the display of individual student performance in school. Based on the SI of article citations, we used KIDMAP to determine whether China dominates the field of CS.

METHODS

The data were derived from published research in the Web of Science, which included 199 countries and 254 subject categories (SC, between 2010 and 2019). A total of 96 SC related to biomedicine were extracted. We examined 7 factors associated with CS using exploratory factor analysis. Based on the SI in CS under the Rasch model, 1-dimensional SCs on CS were displayed on Wright Maps and KIDMAPs. An analysis of the dominance of CS in China was presented on the basis of a scatter plot.

RESULTS

Our findings indicate that (1) CS domains are divided into 2 groups (traditional and advanced domains); (2) no evidence has been found that China dominates CS; based on SI indicators, China was ranked third with --2.62 and 0.79 logits after Taiwan and Slovenia (-(-2.62 and 9.24 logits in Factors 1 and 2) in the period from 2010 to 2019.

CONCLUSIONS

There is insufficient evidence to demonstrate that China has a dominant role over other countries/regions despite ranking third in CS. In future studies, it is recommended to include a KIDMAP visual to assess dominant roles in other areas of research, rather than to confine ourselves to CS as we did in this study.

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.