Malignant temporal bone tumors (1941-2025): A bibliometric analysis of publication trends, key contributors, and thematic evolution.

BackgroundGlioblastoma (GBM) is one of the most aggressive neurogenic tumors. Despite advances in treatment, the presence of the blood-brain barrier (BBB) continues to pose significant challenges to effective therapeutic delivery. However, to date, no comprehensive bibliometric analysis has systematically evaluated the relationship between GBM and the BBB over the past three decades.ObjectiveThis study provides an overview of research progress on GBM and the BBB, with emphasis on structural and functional changes of the BBB. It also identifies current research hotspots, predicts emerging trends, and offers insights for future investigations. Method: Literature from the past 30 years was retrieved from the Web of Science Core Collection and PubMed. Bibliometric analysis was performed using the R programming language, and data visualization was conducted with VOSviewer, CiteSpace, and Tableau.ResultSince 2015, publication output and academic influence in this field have increased exponentially. The United States leads in both publication volume and citation count, and engages in extensive international collaborations. Cancer Research, the leading journal in this field, ranks first with 6,775 citations over the past 30 years. Keyword analysis reveals that the field has primarily focused on tumor-associated angiogenesis, the role of vascular endothelial growth factor (VEGF) in BBB disruption, optimization of drug delivery strategies, the influence of the tumor microenvironment (TME) on tumor progression, and advances in precision medicine. Co-citation analysis, citation burst detection, and Latent Dirichlet Allocation (LDA) topic modeling have identified seminal publications and key developmental trajectories. Notably, a comprehensive analysis of clinical trial literature revealed a gradual shift in research focus from traditional morphological and single-agent efficacy studies to more integrated approaches, including BBB permeability regulation, targeted drug delivery, and multimodal functional imaging.ConclusionThis study offers a comprehensive overview of GBM−BBB research trends over the past 30 years. It advances the understanding of their interplay and provides theoretical guidance for overcoming the BBB and improving GBM outcomes.

This study aims to evaluate 16,891 academic publications in the field of cinema between 1980 and 2024 using bibliometric analysis and topic modeling methods. Based on data obtained from the Web of Science (WOS) and Scopus databases, bibliometric findings were received, including the distribution of publications by year, the annual number and rate of citations per article, the most productive authors in the field, the production status of authors over time, the countries of authors and the number of articles they published, and the journals with the highest number of publications. Data obtained from the Web of Science (WOS) and Scopus databases were also used to identify prominent word groups and themes in the articles using text mining and Latent Dirichlet Allocation (LDA) topic modeling. As a result of the analysis, 12 main themes emerged based on word-text relationships and the weight of publications. The findings show that cinema studies have developed with increasing momentum over the years and that there has been a growing focus on certain topics. This study systematically examines the development of cinema studies literature through descriptive content analysis and LDA topic modeling. In this respect, it is important in that it systematically reveals the structural and thematic transformation of academic production in the field of cinema and provides a theoretical and methodological basis for future research. It also makes a current and multidimensional contribution to the discipline in terms of revealing the increasingly important digital trends, cultural representations, and interdisciplinary developments in cinema studies.

BackgroundThe exponential growth of artificial intelligence (AI) in healthcare has raised critical ethical concerns.AimsTo systematically identify research hotspots and trends of AI ethics in the medical field and provide evidence-based insights for future research.MethodsA bibliometric analysis was conducted on publications from the Web of Science Core Collection (WOSCC) and China National Knowledge Infrastructure (CNKI) up to August 24, 2025. Visualisation tools were used to map the publication trend, as well as author, country, institution, journal, and keyword distributions. Keyword co-occurrence networks, clustering and burst analysis were employed to identify research hotspots and evolving trends. The results are reported following the BIBLIO checklist.ResultsA total of 1034 publications (291 from CNKI, 743 from WOSCC) were included. Key journals were Chinese Medical Ethics (CNKI) and Journal of Medical Ethics (WOSCC). China (n = 291) and the USA (n = 174) were the top publishing countries in CNKI and WOSCC, respectively. Leading institutional output came from developed countries (e.g., Harvard University, University of London, University of Toronto). Four research hotspots were identified: 1) ethical issues in different application scenarios of AI techniques in clinical practice, 2) AI ethical concerns and considerations in medical academia, 3) ethical challenges in AI-driven medical education, and 4) ethical governance, supervision, and review in healthcare. Keyword burst analysis indicated an increasing attention on balancing AI development with ethical governance, promoting research on complex intelligent tools embedded with ethical principles.ConclusionAttention to this topic has grown steadily over the past eight years. Research trends reflect a shift from fragmentation to the integration of technology and ethics in medicine. Future research should further refine the operational definitions of ethical principles across diverse application scenarios to guide stakeholders in effectively integrating technology and ethics.

BackgroundChildhood learning disabilities and neurodevelopmental disorders have been increasingly linked to early-life environmental chemical exposures, including air pollutants, heavy metals, endocrine-disrupting chemicals, and pesticides. Despite growing academic interest, a comprehensive analysis of global research trends and emerging themes in this interdisciplinary field remains lacking.MethodsA bibliometric analysis was conducted using the Web of Science Core Collection (WoSCC) database to identify studies published between January 2005 and December 2025. Articles and reviews written in English focusing on environmental exposure and childhood learning disabilities were included. CiteSpace software was employed to analyze annual publication trends, country and institutional contributions, co-authorship networks, co-cited references, and keyword clustering and evolution. In addition, we applied Latent Dirichlet Allocation (LDA) topic modeling to abstracts/keywords to uncover latent thematic structures and quantify topic prevalence across the corpus.ResultsA total of 1056 publications were included. Global research output increased steadily, with a notable surge after 2017. The United States led in publication volume and international collaboration, followed by China, the United Kingdom, and Spain. Influential institutions included Harvard University, Columbia University, and ISGlobal. Key authors such as Jordi Sunyer, David Bellinger, and Brenda Eskenazi were identified as central contributors. Frequently co-cited journals included Environmental Health Perspectives and Environmental Research. Major research clusters focused on air pollution, endocrine disruptors, oxidative stress, and neurodevelopmental disorders. Timeline and burst analyses revealed a shift from traditional toxicants (e.g., lead, mercury) to complex outcomes such as academic performance and mental health, with growing attention to mechanisms like epigenetics and environmental justice. LDA topic modeling revealed 15 themes spanning exposure settings (indoor/residential/air pollution), neurodevelopmental outcomes (autism/ADHD/cognition), and key neurotoxicants (pesticides/PCB, arsenic, methylmercury), suggesting an evolving focus toward functional outcomes and mechanisms.ConclusionsThis study highlights the evolving landscape of research linking environmental exposures to childhood cognitive and behavioral outcomes. The field is expanding from exposure identification to mechanistic understanding and real-world functional implications. Greater interdisciplinary collaboration and equity-focused research are needed to inform policy and protect child brain health globally.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41182-026-00949-z.

This study aims to explore recent literature on gestational diabetes mellitus (GDM) screening, assessment, and monitoring and identifying research hotspots and future trends. In this study, bibliometric methods were employed to analyze the literature related to the screening, assessment, and monitoring of GDM retrieved from the Web of Science Core Collection (WoSCC). Specifically, the analysis focused on the annual publication and citation trends of relevant literature, collaborative networks involving countries, institutions, authors, and journals, keyword co-occurrence analysis, reference co-citation analysis, historical evolution of the research field, and topic modeling. The results show a 50% rise in publications on gestational diabetes over the past 5 years, with the field experiencing distinct developmental stages from 1961 to 2026. China ranks first in global publication volume, while the United States leads in citation impact and international collaboration intensity. Keyword analysis identified three core clusters and a "three jumps and two rises" evolution pattern of citation bursts, with machine learning and adverse pregnancy outcomes emerging as ongoing high-burst-strength keywords. Latent Dirichlet allocation (LDA) topic modeling classified 16 optimal topics into four groups: Screening and Diagnostic Approaches, Pathophysiology and Molecular Mechanisms, Environmental, Social and Behavioral Determinants, and Clinical Management, Complications and Health Outcomes. The focus of GDM screening, assessment, and monitoring is shifting from traditional oral glucose tolerance test (OGTT)-based diagnosis to biomarker-based early prediction and AI-driven digital monitoring throughout pregnancy, highlighting the importance of patient characteristics and risk factors. Future research will contribute to improving clinical practices for gestational diabetes and enhancing maternal and infant health. This study's integrated bibliometric and LDA topic modeling approach clarifies the knowledge structure and evolutionary trends of the GDM screening-assessment-monitoring continuum, providing targeted new perspectives for further exploration in related fields.

Introduction: Telemedicine has become a vital component of Taiwan's health care system, enhancing access, efficiency, and equity in medical services. Rapid growth has been fueled by advances in digital technologies, policy support, and increasing clinical applications, particularly during the COVID-19 pandemic. Despite this progress, a comprehensive overview of research trends is lacking. This study applies bibliometric and quantitative analyses to map current developments, identify key contributors, and guide future health care policy and practice. Methods: We conducted a bibliometric analysis of telemedicine research in Taiwan using the Web of Science Core Collection (to August 30, 2025). Publication characteristics, citation counts, and authorship patterns were analyzed. Keyword co-occurrence networks were generated with VOSviewer to identify research hotspots and thematic clusters, ensuring accuracy through independent verification. Results: From 1998 to 2025, telemedicine research in Taiwan has shown steady growth, with publication counts rising sharply after 2010 and peaking in 2024. Key contributors include National Taiwan University, National Cheng Kung University, and Taipei Medical University, which lead in both publication volume and citation impact. Highly cited studies address telemedicine applications, COVID-19 responses, and digital health innovations. Co-occurrence network analysis highlights four major research themes: clinical telemedicine, health care management and policy, COVID-19-related digital health, and AI-driven technologies. Challenges remain in rural areas, including infrastructure, technology acceptance, and workforce shortages, underscoring the need for targeted strategies to expand telemedicine. Conclusions: Future telemedicine research in Taiwan should prioritize rural health care, leveraging 5G, AI, and smart technologies to enhance care efficiency, accuracy, and resource allocation, supporting sustainable, high-quality, and equitable medical services aligned with Environmental, Social, and Governance principles.

Although the rapid development of Artificial Intelligence (AI) in recent years has brought increasing academic attention to the intelligent transformation of physical education, the core knowledge structure of this field, such as its primary research topics, has yet to be systematically explored. The LDA (latent Dirichlet allocation) topic model can identify latent themes in large-scale textual data, helping researchers extract key research directions and development trends from extensive literature. This study is based on data from the Web of Science Core Collection and employs a systematic literature screening process, utilizing the LDA topic model for in-depth analysis of relevant literature to reveal the current status and trends of AI technology in physical education. The findings indicate that AI applications in this field primarily focus on three areas: “AI and data-driven optimization of physical education and training”, “computer vision and AI-based movement behavior recognition and training optimization”, and “AI and virtual technology-driven innovation and assessment in physical education”. An in-depth analysis of existing research shows that the intelligentization of physical education, particularly in school and athletic training contexts, not only promotes sustainable development in the field but also significantly enhances teaching quality and safety, allowing educators to utilize data more precisely to optimize teaching strategies. However, current research remains relatively broad and lacks more precise and robust data support. Therefore, this study critically examines the limitations of current research in the field and proposes key research directions for further advancing the intelligent transformation of physical education, providing a solid theoretical framework and guidance for future research.

Physical bookstore is the leader of cultural trend, the carrier of national reading and the provider of public cultural services, which embodies the cultural soft power of a city. The widely use of Internet e-commerce platform and the change of people's reading habits have brought great impact on physical bookstores, resulting in poor overall profitability of physical bookstores. In order to realize the sustainable development of physical bookstores, we mine and analyze consumer-generated online reviews. In this paper, a method of sentiment analysis based on Hybrid LSTM-CNN (Hybrid Long Short-Term Memory-Convolutional Neural Network) and LDA (Latent Dirichlet Allocation) topic model is proposed. Firstly, the Hybrid LSTM-CNN model is used to classify reviews, and then LDA topic model is used to extract features of positive and negative reviews. The results show that Hybrid LSTM-CNN model has better performance than the classic LSTM and CNN in sentiment classification. The LDA model mines that consumers have the positive attitude towards the products, context and ambiance of physical bookstores, and the negative attitude towards price and service. This method studies consumer-generated online reviews in physical bookstores from two aspects: sentiment classification and topic mining, which can help physical bookstore operators to know consumer feedback in time.

Background/Objectives: Dental trauma is common in childhood and may negatively affect oral health-related quality of life (OHRQoL). Given the growing volume and diversity of publications on this topic, a bibliometric approach is suitable for mapping scientific production, collaboration patterns, thematic evolution, and citation dynamics. This study aimed to perform a bibliometric analysis of the literature addressing the impact of dental trauma on OHRQoL in a paediatric population up to 14 years of age. Methods: A bibliometric study was conducted using Clarivate's Web of Science Core Collection (WoS-CC), selected for its standardized citation indexing and suitability for bibliometric analyses. Publications retrieved up to August 2025, without restrictions on language or year, were analyzed using VOSviewer (version 1.6.20) and Biblioshiny (Bibliometrix package). Indicators included scientific output, collaboration networks, keyword co-occurrence, thematic evolution, and citation performance. Spearman's correlation was used to explore relationships between citation counts, journal impact factor, and year of publication. Results: A total of 107 articles published between 2006 and 2025 were included. Scientific output increased steadily, with publications concentrated in specific countries, notably Brazil and India. The predominant research focus concerned the impact of dental trauma on children's quality of life. Dental Traumatology was the most productive journal and showed high local citation impact. Citation analysis demonstrated a weak positive correlation between citation counts and journal impact factor (rho = 0.37, p < 0.001) and a strong negative correlation with year of publication (rho = -0.84, p < 0.001). Conclusions: This bibliometric analysis identifies research trends, thematic stability, and collaboration patterns in studies on dental trauma and OHRQoL in children, highlighting regional concentration and limited international collaboration.

Background/Aim. Vojnosanitetski pregled (VSP) is the official scientific and professional journal of the University of Defence in Belgrade, Serbia. VSP is a peer-reviewed journal that publishes a wide range of scientific and professional articles. The aim of the study was to perform a bibliometric analysis of the 200 most cited articles published in VSP, and to assess the impact, significance, and scientific contribution of the journal. Methods. Using the Web of Science (WoS) Core Collection (WoSCC) database, 2,664 articles published from 2008 to December 31, 2022, were analyzed, focusing on citation counts, author productivity, and collaboration networks. Results. The analysis revealed that the majority of the most cited articles came from Serbian institutions, with the University of Belgrade being the most prolific contributor. The top five most cited authors as well as the most cited article were identified, and a trend of increasing significance of research on bioactive compounds was noticed. The study also observed a shift in key word usage over time, reflecting changes in research trends within the journal. The collaboration network analysis showed a strong clustering among the leading authors, indicating a collaborative culture that contributes to the journal?s influence. Conclusion. Emphasizing the importance of targeted collaborations makes it evident that maintaining and expanding the journal?s impact on the scientific community is the key to improving medical research. Despite limitations such as reliance on a single database, the findings provide valuable insights into the journal?s role in advancing medical research and highlight the importance of targeted collaboration.

BackgroundMpox has reemerged as a global public health concern. With the growing reliance on social media for health information dissemination, understanding public perception through these platforms is essential for designing effective health promotion strategies.ObjectiveThis study analyzes TikTok data related to mpox using Latent Dirichlet Allocation (LDA) topic modeling. This paper aims to extract key topics and inform targeted health promotion strategies for mpox prevention and control.MethodsUsing the “Aisou Jisou” system, we collected TikTok data containing the keyword “Mpox” from April 1, 2022, to March 31, 2025. The dataset comprised 25,672 text data and associated search terms. We analyzed trends in the Search Index and Target Group Index (TGI) across time, gender, age groups, and provinces. LDA topic modeling was applied to identify latent topics within the text data, and topic evolution was examined during 4 peak months of the Search Index.ResultsA total of 4 major Search Index peaks were identified on TikTok in China, which are May 2022, July 2023, August 2024, and February 2025. These peaks aligned with key global and national mpox events, including WHO’s declaration of a global mpox outbreak in May 2022 and the detection of the clade Ib Mpox in China in January 2025. TGI analysis revealed that users aged 18‐23 years exhibited the highest engagement. Spatially, Beijing, Tianjin, and Jilin recorded the highest cumulative TGI values (5922.38, 5692.41, and 3579.90, respectively). LDA topic modeling identified 8 primary topics, including transmission and prevention, vaccine concerns, and misinformation, etc. Public attention evolved from general disease knowledge toward issues of stigmatization and vaccine distrust over time. Sankey diagrams illustrated shifts in public attention across topics at different Search Index peaks, with “Mpox Transmission and Prevention” receiving the most attention in May 2022 and “Mpox Vaccination and Infection Prevention” in February 2025.ConclusionsTikTok provides real-time insights into public attention during mpox outbreaks, but can also propagate misinformation and stigmatizing narratives. Public health authorities should leverage these platforms for timely communication, actively address misinformation, and mitigate social bias. Tailored strategies are needed to enhance health literacy, minimize stigma, and strengthen outbreak preparedness and response. This study highlights the dual role of social media as both an information source and a potential vector for misinformation, emphasizing the necessity for active monitoring and regulation by health authorities to ensure the accuracy and reliability of disseminated health information.

This study aimed to systematically map and analyze the scientific literature on the cyclic fatigue resistance of nickel-titanium (NiTi) rotary instruments, using bibliometric and science mapping techniques. Eight hundred eighty-one publications indexed in the Web of Science Core Collection between 1980 and 2024 were analyzed. Bibliometric performance indicators, keyword co-occurrence networks, thematic maps, and co-citation analyses were utilized. Data were processed using Bibliometrix, a comprehensive R-based science mapping software. Trends in publication volume, author and journal productivity, influential articles, thematic evolution, and international collaboration patterns were evaluated. There has been a steady increase in publication output over the last four decades, with a significant acceleration after 2010. Thematic mapping revealed that keywords such as "cyclic fatigue," "reciprocation," "heat treatment," and "fracture resistance" have been central to the field. Citation analysis identified key foundational studies frequently referenced in fatigue testing methodology. International research collaborations were most prominent among North American, East Asia, and European institutions. This bibliometric study underscores the clinical importance of innovations in NiTi rotary instrumentation, particularly for enhancing cyclic fatigue resistance. By identifying key research trends and thematic clusters, the study provides valuable guidance for clinicians and researchers aiming to optimize instrument durability, reduce procedural complications, and advance endodontic practice.

Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

Nowadays the explosion of Web information has led to the boom of massive web documents such as news webpages, online literature, etc. The latent topics behind the documents spread by self-evolution and mutual transition. Understanding how topics in documents evolve and transit is an important and challenging problem. Topic model is a set of powerful toolkits to model documents generation to find their underlying topics, usually at the unigram level, making it difficult to model the relationship between terms and their underlying topics. In this paper, we propose a pairwise topic modeling method to incorporate a pairwise relationship into topic modeling methods. We manage to discover latent topics as well as topic transitions at the same time in a natural way. We show that the pairwise topic model can facilitate discovering of individual topics as well as topic evolution. The results indicate our proposed method leads to a significant performance improvement over the traditional topic modeling methods, such as Latent Dirichlet Allocation (LDA) in terms of language perplexity. Besides, we conduct a series of empirical studies to show the topic words and topic transitions discovered. From the case studies, we show that with the help of PTM methods, people are able to explicitly understand how topics evolve and transit between each other.

The aim of this bibliometric study was to systematically map the evolution, structural characteristics and methodological profile of artificial intelligence (AI) research in endodontics by analysing publication trends, collaboration networks, thematic development and citation impact. A bibliometric analysis was conducted using publications indexed in the Web of Science Core Collection, Scopus and PubMed from 1 January 1990 to 19 August 2025. Following deduplication and eligibility screening, 245 articles were included. Authorship, country-level collaboration and keyword co-occurrence networks were analysed using VOSviewer. Citation data were harmonised across databases using regression-based normalisation. Negative binomial regression was applied to evaluate the association between citation counts and publication year, document type and open-access status. AI-related research in endodontics showed minimal activity before 2020, followed by rapid growth driven predominantly by deep learning (DL) based imaging applications. Periapical radiographs (PA) and cone-beam computed tomography (CBCT) were the most frequently used data sources. China accounted for the highest publication volume, whereas the United States demonstrated the greatest citation-weighted influence and centrality within international collaboration networks. Keyword co-occurrence analysis identified six thematic clusters, dominated by radiographic diagnostics, with a recent emergence of natural language processing and generative AI applications. Publication year was the only significant predictor of citation counts (p < 0.001); document type and open-access status were not significantly associated. AI research in endodontics has evolved into a rapidly expanding, imaging-centred research domain characterised by increasing output but limited methodological diversity, restricted use of explainable AI and inconsistent adoption of reporting guidelines. These findings provide a structured overview of the field's development and current research profile.