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.

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.

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.

Visual impact beam plots: Analyzing research profiles and bibliometric metrics using the following-leading clustering algorithm (FLCA).

BACKGROUND: A new approach to showcasing author publications on a website involves using a visual representation instead of the conventional paper list. The creation of an impact beam plot (IBP) as a research profile for individuals is crucial, especially when incorporating collection edges that include self-cited articles through a rare cluster analysis technique not commonly found in the literature. This study presents the application of a unique method called the following-leading clustering algorithm (FLCA) to generate IBPs for 3 highly productive authors. METHODS: For the 3 highly productive authors, Sung-Ho Jang from South Korea, Chia-Hung Kao from Taiwan, and Chin-Hsiao Tseng from Taiwan, all their published articles indexed in the Web of Science Core Collection were downloaded. Sung-Ho Jang published 593 articles, Chia-Hung Kao published 732 articles, and Chin-Hsiao Tseng published 160 articles. To analyze and showcase their publications, the FLCA was utilized. This algorithm helped cluster their articles and identify representative publications for each author. To assess the effectiveness and validity of the FLCA algorithm, both network charts and heatmaps with dendrograms were employed. IBPs were then created and compared for each of the 3 authors, taking into consideration their h-index, x-index, and self-citation rate. This allowed for a comprehensive visual representation of their research impact and citation patterns. RESULTS: The results show that these authors' h-index, x-index, and self-citation rates were (37, 44.01, 1.66%), (42, 61.47, 0.23%), and (37, 40.3, 6.62%), respectively. A higher value in these metrics indicates a more remarkable research achievement. A higher self-citation rate with a lower cluster number indicates that manuscripts are more likely to have been self-drafted. Using the FLCA algorithm, IBPs were successfully generated for each author. CONCLUSION: The FLCA algorithm allows for the easy generation of visual IBPs based on authors' publication profiles. These IBPs incorporate 3 important bibliometric metrics: h-index, x-index, and self-citations. These metrics are highly recommended for use by researchers globally, particularly with the self-citation rate, as they offer valuable insights into the scholarly impact and citation patterns of individual researchers.

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.

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.

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.

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.

Assessing the impact of COVID-19 on outpatient and inpatient revenues: A comparative analysis of large and small hospitals in Taiwan.

BACKGROUND: The COVID-19 pandemic has had profound effects on healthcare systems worldwide, not only by straining medical resources but also by significantly impacting hospital revenues. These economic repercussions have varied across different hospital departments and facility sizes. This study posits that outpatient (OPD) revenues experienced greater reductions than inpatient (IPD) revenues and that the financial impact was more profound in larger hospitals than in smaller hospitals. METHODS: We collected data on patient case numbers and associated revenues for 468 hospitals from the Taiwan government-run National Health Insurance Administration website. We then employed Microsoft Excel to construct scatter plots using the trigonometric function (=DEGREES (Atan (growth rate))) for each hospital. Our analysis scrutinized 4 areas: the case numbers and the revenues (represented by medical fees) submitted to the Taiwan government-run National Health Insurance Administration in both March and April of 2019 and 2020 for OPD and IPD departments. The validity of our hypotheses was established through correlation coefficients (CCs) and chi-square tests. Moreover, to visualize and substantiate the hypothesis under study, we utilized the Kano diagram. A higher CC indicates consistent counts and revenues between 2019 and 2020. RESULTS: Our findings indicated a higher impact on OPDs, with CCs of 0.79 and 0.83, than on IPDs, which had CCs of 0.40 and 0.18. Across all hospital types, there was a consistent impact on OPDs (P = .14 and 0.46). However, a significant variance was observed in the impact on IPDs (P < .001), demonstrating that larger hospitals faced greater revenue losses than smaller facilities, especially in their inpatient departments. CONCLUSION: The two hypotheses confirmed that the COVID-19 pandemic impacted outpatient departments more than inpatient departments. Larger hospitals in Taiwan faced greater financial challenges, especially in inpatient sectors, underscoring the pandemic's varied economic effects. The COVID-19 pandemic disproportionately affected outpatient departments and larger hospitals in Taiwan. Policymakers must prioritize support for these areas to ensure healthcare resilience in future epidemics. The research approach used in this study can be utilized as a model for similar research in other countries affected by COVID-19.

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.

An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel.

BACKGROUND: Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument. METHODS: A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC. RESULTS: We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study. CONCLUSION: On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

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.

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.

A comparison of 3 productive authors' research domains based on sources from articles, cited references and citing articles using social network analysis.

BACKGROUND: An individual's research domain (RD) can be determined from objective publication data (e.g., medical subject headings and Medical Subject Headings (MeSH) terms) by performing social network analysis. Bibliographic coupling (such as cocitation) is a similarity metric that relies on citation analysis to determine the similarity in RD between 2 articles. This study compared RD consistency between articles as well as their cited references and citing articles (ARCs). METHODS: A total of 1388 abstracts were downloaded from PubMed and authored by 3 productive authors. Based on the top 3 clusters in social network analysis, similarity in RD was observed by comparing their consistency using the major MeSH terms in author articles, cited references and citing articles (ARC). Impact beam plots with La indices were drawn and compared for each of the 3 authors. RESULTS: Sung-Ho Jang (South Korea), Chia-Hung Kao (Taiwan), and Chin-Hsiao Tseng (Taiwan) published 445, 780, and 163 articles, respectively. Dr Jang's RD is physiology, and Dr Kao and Dr Tseng's RDs are epidemiology. We confirmed the consistency of the RD terms by comparing the major MeSH terms in the ARC. Their La indexes were 5, 5, and 6, where a higher value indicates more extraordinary research achievement. CONCLUSION: RD consistency was confirmed by comparing the main MeSH terms in ARC. The 3 approaches of RD determination (based on author articles, the La index, and the impact beam plots) were recommended for bibliographical studies in the future.