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Chou W, Chow JC. Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps. Medicine (Baltimore) 2024; 103:e38686. [PMID: 38996096 PMCID: PMC11245264 DOI: 10.1097/md.0000000000038686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/14/2024] Open
Abstract
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.
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Affiliation(s)
- Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan 710, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung 400, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Chung San Medical University, Taichung 400, Taiwan
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Singh A, Rejeb A. Illness perception: A bibliometric study. Heliyon 2024; 10:e31805. [PMID: 38845980 PMCID: PMC11153196 DOI: 10.1016/j.heliyon.2024.e31805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
Illness perception is a crucial area of study that has seen significant growth and development over the years. This study conducts a comprehensive bibliometric and network analysis of illness perception research, capturing its evolution from 1975 to 2023. Utilizing 1813 publications from the Scopus database, authored by 5428 researchers, we identify key scholars and influential articles in the field. Our analysis includes various bibliometric networks such as citation, co-citation, collaboration, and keyword co-occurrence networks, along with the presentation of intellectual structure maps. Major research areas include the role of illness perception in mental health conditions like depression, coping mechanisms, quality of life, and chronic illnesses like diabetes, as well as the influence of lay beliefs on health behaviors, and the impact of illness beliefs on conditions like Myocardial Infarction and stroke. The results show a growing academic interest in understanding how illness perceptions shape healthcare outcomes and behaviors.
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Affiliation(s)
- Arti Singh
- Jindal School of Psychology and Counseling, O.P Jindal Global University, Sonipat, Haryana-131029, India
| | - Abderahman Rejeb
- Faculty of Business Economics, Széchenyi István University, 9026 Győr, Hungary
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Ho SYC, Chow JC, Chou W. Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals. Medicine (Baltimore) 2024; 103:e36219. [PMID: 38241539 PMCID: PMC10798765 DOI: 10.1097/md.0000000000036219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/30/2023] [Indexed: 01/21/2024] Open
Abstract
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.
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Affiliation(s)
- Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Lin HY, Chou W, Chien TW, Yeh YT, Kuo SC, Hsu SY. Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD). Medicine (Baltimore) 2024; 103:e36547. [PMID: 38241545 PMCID: PMC10798733 DOI: 10.1097/md.0000000000036547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/17/2023] [Indexed: 01/21/2024] Open
Abstract
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.
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Affiliation(s)
- Hsin-Ying Lin
- Department of Ophthalmology, Chi-Mei Medical Center, Yong Kang, Tainan City, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Yu-Tsen Yeh
- Medical School, St. George’s, University of London, United Kingdom
| | - Shu-Chun Kuo
- Department of Optometry, Chung Hwa University of Medical Technology, Tainan, Taiwan
- Department of Ophthalmology, Chi-Mei Medical Center, Yong Kang, Tainan City, Taiwan
| | - Sheng-Yao Hsu
- Department of Optometry, Chung Hwa University of Medical Technology, Tainan, Taiwan
- Department of Ophthalmology, An Nan Hospital, China Medical University, Tainan, Taiwan
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Wu AL, Chou W. Identifying China's distinctive academic fields among the top 2% of influential scientists: A bibliometric analysis using Rasch KIDMAP. Medicine (Baltimore) 2024; 103:e36706. [PMID: 38181244 PMCID: PMC10766269 DOI: 10.1097/md.0000000000036706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 11/27/2023] [Indexed: 01/07/2024] Open
Abstract
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.
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Affiliation(s)
- Alice-Like Wu
- Department of Medical Statistics and Analytics, Coding Research Center, Toronto, Canada
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- 10 Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Ho SYC, Chien TW, Chou W. Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e34511. [PMID: 38115345 PMCID: PMC10727539 DOI: 10.1097/md.0000000000034511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 12/21/2023] Open
Abstract
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.
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Affiliation(s)
- Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Geriatrics and Gerontology, ChiMei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan 710, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Cheng YZ, Lai TH, Chien TW, Chou W. 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. Medicine (Baltimore) 2023; 102:e36154. [PMID: 38065864 PMCID: PMC10713138 DOI: 10.1097/md.0000000000036154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/26/2023] [Indexed: 12/18/2023] Open
Abstract
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.
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Affiliation(s)
- Yung-Ze Cheng
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tzu-Han Lai
- Grade Two in Senior High School, National Tainan Second Senior High School, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Chuang HY, Ho SYC, Chou W, Tsai CL. Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis. Medicine (Baltimore) 2023; 102:e36475. [PMID: 38050200 PMCID: PMC10695623 DOI: 10.1097/md.0000000000036475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023] Open
Abstract
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.
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Affiliation(s)
- Hua-Ying Chuang
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
- Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan
- Department of Nursing, Chung Hwa University, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
| | - Chia-Liang Tsai
- Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan
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Lai PC, Chou W, Chien TW, Lai FJ. A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis. Medicine (Baltimore) 2023; 102:e34801. [PMID: 37933006 PMCID: PMC10627629 DOI: 10.1097/md.0000000000034801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 07/27/2023] [Indexed: 11/08/2023] Open
Abstract
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.
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Affiliation(s)
- Po-Chih Lai
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan 710, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Feng-Jie Lai
- Department of Dermatology, Chi-Mei Hospital, Tainan, Taiwan
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Hsiung C, Chou W, Chien TW, Chou PH. Differences in productivity and collaboration patterns on spine-related research between neurosurgeons and orthopedic spine surgeons: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e35563. [PMID: 37861477 PMCID: PMC10589607 DOI: 10.1097/md.0000000000035563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
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.
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Affiliation(s)
- Chun Hsiung
- Department of Education, Chang Gung Memorial Hospital, Linkou, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei medical center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Po-Hsin Chou
- Department of Orthopedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University Taipei, Taipei, Taiwan
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Cheng TY, Ho SYC, Chien TW, Chow JC, Chou W. A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis. Medicine (Baltimore) 2023; 102:e35156. [PMID: 37861508 PMCID: PMC10589539 DOI: 10.1097/md.0000000000035156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/18/2023] [Indexed: 10/21/2023] Open
Abstract
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.
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Affiliation(s)
- Teng-Yun Cheng
- Department of Emergency Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Shiao CC, Wu JT, Chu YC, Tang YH, Huang L, Lai HY. Bibliometric analysis of the top 100 most-cited articles on video laryngoscope from 2011 to 2022. J Chin Med Assoc 2023; 86:902-910. [PMID: 37683127 DOI: 10.1097/jcma.0000000000000981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND The popularity of video laryngoscope (VL) has increased rapidly since its introduction in the late 1990s. However, a comprehensive overview of VLs evolution and impact is lacking, which merits further investigation. METHODS We conducted a bibliometric analysis of the top 100 most-cited articles on VL (Top100VL) published between 2011 and 2022 and retrieved from the PubMed and Web of Science databases. Using social network analysis, we identified the subject terms and subject categories of the Top100VL and compared their citation counts across individual subject terms and categories via one-way analysis of variance (ANOVA). Then, we employed the Medical Query Expert software to assess the practical applications of VL. RESULTS The Top100VL included 65 subjects across nine subject categories, with "anesthesiology" being the most frequently represented category and "coronavirus infections" with the highest impact factor. The citation counts inferred by subject categories significantly correlated with the actual citation counts (Pearson's R = 0.4; p < 0.01). For enhanced visualization, we employed network visualization and Sankey diagrams to display the article characteristics. We highlighted the clinical advantages of VL, including its usefulness in difficult intubations, wider angle of view, and management of pediatric emergencies, as well as its teaching benefits, such as facilitating training programs and a lower learning curve. CONCLUSION By using bibliometric analysis and natural language processing methods, we effectively summarized the applications of VL in both clinical and teaching settings, particularly in reducing the risk of cross-infection during the Coronavirus Disease 2019 pandemic.
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Affiliation(s)
- Chih-Chung Shiao
- Division of Nephrology, Department of Internal Medicine, Camillian Saint Mary's Hospital Luodong, Yilan, Taiwan, ROC
| | - Jui-Teng Wu
- Department of Surgery, Camillian Saint Mary's Hospital Luodong, Yilan, Taiwan, ROC
| | - Ya-Chun Chu
- Department of Anesthesiology, Taipei Veterans General Hospital, and National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Yu-Hsuan Tang
- School of Life Science, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | | | - Hsien-Yung Lai
- Department of Anesthesiology, Mennonite Christian Hospital, Hualien, Taiwan, ROC
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Lin CK, Ho SYC, Chien TW, Chou W, Chow JC. Analyzing author collaborations by developing a follower-leader clustering algorithm and identifying top co-authoring countries: Cluster analysis. Medicine (Baltimore) 2023; 102:e34158. [PMID: 37478228 PMCID: PMC10662898 DOI: 10.1097/md.0000000000034158] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/09/2023] [Indexed: 07/23/2023] Open
Abstract
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.
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Affiliation(s)
- Che-Kuang Lin
- Department of Cardiology, Chiali Chi-Mei Hospital, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Geriatrics and Gerontology, ChiMei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Yen PC, Chou W, Chien TW, Jen TH. Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm (FLCA): A bibliometric study. Medicine (Baltimore) 2023; 102:e34169. [PMID: 37390236 PMCID: PMC10313307 DOI: 10.1097/md.0000000000034169] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
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.
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Affiliation(s)
- Pei-Chun Yen
- Department of Hepatobiliary Gastroenterology, Chiali Chi-Mei Hospital, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tung-Hui Jen
- Department of Senior Welfare and Service, Southern Taiwan University of Science and Technology, Tainan, Taiwan
- Department of Chinese Medicine, Chi-Mei Medical Center, Tainan, Taiwan
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Liang YE, Ho SYC, Chien TW, Chou W. Analyzing the number of articles with network meta-analyses using chord diagrams and temporal heatmaps over the past 10 years: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e34063. [PMID: 37352064 PMCID: PMC10289580 DOI: 10.1097/md.0000000000034063] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/26/2023] [Accepted: 06/01/2023] [Indexed: 06/25/2023] Open
Abstract
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.
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Affiliation(s)
- Yu-Erh Liang
- Department of Chinese Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Sam Yu-Chieh Ho
- Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Geriatrics and Gerontology, ChiMei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Hung CC, Tu MY, Chien TW, Lin CY, Chow JC, Chou W. The model of descriptive, diagnostic, predictive, and prescriptive analytics on 100 top-cited articles of nasopharyngeal carcinoma from 2013 to 2022: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e32824. [PMID: 36820592 PMCID: PMC9907932 DOI: 10.1097/md.0000000000032824] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
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.
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Affiliation(s)
- Chung-Chia Hung
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
| | - Mei-Yu Tu
- Department of Nutrition, Chi Mei Medical Center, Tainan, Taiwan
- Department of Food Nutrition, Chung Hwa University of Medical Technology, Tainan, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Cheng-Yao Lin
- Division of Hematology-Oncology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
- Department of Senior Welfare and Services, Southern Taiwan University of Science and Technology, Tainan, Taiwan
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
- * Correspondence: Willy Chou, Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan 710, Taiwan (e-mail: )
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Tam HP, Hsieh WT, Chien TW, Chou W. A leading bibliometric author does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis. Medicine (Baltimore) 2023; 102:e32609. [PMID: 36637941 PMCID: PMC9839291 DOI: 10.1097/md.0000000000032609] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
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.
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Affiliation(s)
- Hon-Pheng Tam
- Department of Emergency Medicine, Liouying Chi Mei Medical Center, Tainan, Taiwan
| | - Wan-Ting Hsieh
- Department of Palliative Medicine, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tsair-Wei Chien
- Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
- * Correspondence: Willy Chou, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist., Tainan 710, Taiwan (e-mail: )
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