Scientometric Analysis Research Articles

A spatial scientometric analysis of urban planning publications from 2010–2020: geographic disparities in urban planning knowledge production

PurposeThis paper analyzes and illustrates the spatial distribution of publications in international urban planning journals from 2010 to 2020.Design/methodology/approachThe study employs the Biblioshiny bibliometrix package in R to analyze 44,123 articles from 95 international planning journals. To conduct the spatial scientometric analysis, we adopted the United Nations’ geoscheme focusing on three geographical scales: country, subregion, and region. Collaboration patterns at the country and subregional levels were examined using the VOSviewer tool.FindingsThe study found evidence of a spatial polarization of urban planning scholarly knowledge production. Scholars based in the United States and the United Kingdom consistently published at higher rates than any other country in our data set. The region producing the largest number of publications was Europe, consisting of 39.92% of the total publication count. However, urban planning scholars from the Global South authored only 20.96% of planning publications from 2010–2020. Centralization of planning research is also evident within each region and subregion. As such, both the Global North and the Global South should not be framed as homogenous entities and spatial patterns of knowledge production should not be generalized. The analysis also established the emerging role of Northern America as a major collaborator in inter-country and inter-subregional research collaborations. Co-authorship patterns indicate low intra-regional collaboration in planning research, except for Europe.Originality/valueThis article argues that a culture of exclusivity may be occurring in urban planning publication production. By highlighting the spatial disparities in knowledge production, we emphasize the need to examine the structural and institutional barriers that exclude urban planning knowledge emerging from the peripheries in international planning journals.

Mapping the global knowledge landscape of digital transformation in the AEC industry: a scientometric analysis

PurposeIn recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.Design/methodology/approachEmploying a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.FindingsThe findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.Originality/valueThis study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

A scientometrics analysis and visualization of refractory gastroesophageal reflux disease.

Refractory gastroesophageal reflux disease (refractory GERD) is a heterogeneous disease characterized by unresponsiveness or poor efficacy to proton-pump inhibitors (PPIs). This chronic disorder substantially weakens patients' mental wellbeing and quality of life, increasing the financial burden on society. Multiple articles have been reported in this area. However, literature involving scientometric analysis of refractory GERD is absent. Therefore, it is necessary to understand the evolution of research themes and the main hotspots of refractory GERD through bibliometric methods. All documents related to refractory GERD based on the WOS Core Collection from January 2000 to November 2023 were selected for analysis. Citespace V 6.1 R6, VOSviewer V 1.6.20, and Scimago Graphica V 1.0.38 were used to perform bibliometric analysis. We collected a total of 241 research articles from 36 countries and 322 institutions, contributed by over 1,000 authors. Over the last 20years, the number of articles in this field has increased year by year, and since 2011, the number of publications has increased dramatically, with 85.89% of the papers. These countries are led by the United States and Japan. GUT had the highest number of citations and DIGESTION had the highest number of publications. Research on standardized diagnosis and management, mechanisms, novel monitoring methods, and innovative drugs and procedures for refractory GERD are the main topics and hotspots in this field. This study also found that neuroimmune interaction is closely related to refractory GERD, which may be a new direction for future mechanism research. Our study is the first bibliometric analysis of the global literature on refractory GERD. This research provides valuable insights for researchers, enabling them to quickly understand the research frontier and hot topics of this field.

Bibliometric and scientometric analysis of acarological publications in Türkiye between the years 1992-2023

A bibliometric and scientometric analysis of acarological studies conducted in Türkiye between 1992 and 2023 were performed by searching the Clarivate Thomson Reuters WoS database using 525 keywords. The first publication from Türkiye appeared in WoS in 1992. A total of 1,344 articles, 52 reviews, 30 progress reports, eight letters, eight early access documents, seven editorials, three conference abstracts and one note were found. The 1,453 articles written by Turkish scientists were published in 420 different sources (books, journals, etc.) with an annual growth rate of 16.3% and an average number of references of 10.39. Overall, 149 (10.25%) of the publications were written by a single author, while the remaining 1,304 articles had an average of 4.36 authors and 23.47% of them were written in collaboration with international experts. With some slight fluctuations, the number of publications increased over the years, with the highest number of publications being recorded in 2021 and 2023. Again, a steady increase in total and annual citations was observed, with some slight fluctuations. Systematic and Applied Acarology and International Journal of Acarology were the journals with the highest number of publications, while Experimental and Applied Acarology (n=1,202) was the journal with the highest number of citations to Turkish publications. Experimental and Applied Acarology and Veterinary Parasitology were the journals with the highest H-index. The most prolific authors were Salih Doğan (n=77), İsmail Döker (n=67) and Sultan Çobanoğlu (n=64), while the most cited publications were those of Salih Doğan, Nusret Ayyıldız and Adem Keskin. Münir Aktaş, Salih Doğan and Adem Keskin were the authors with the highest H-index. With 199 publications, Ankara University was the institution with the highest number of publications. The 1,453 publications were produced in collaboration with researchers from 87 countries. The highest number of collaborative publications was with researchers from the United States of America (n=89). The Scientific and Technological Research Council of Türkiye was the institution that founded the highest number of studies.

Developing Standard BIM Execution Plans for Complex Construction Projects

This study addresses the critical need for standardizing building information modeling (BIM) execution plans (BEPs) in the architecture, engineering, construction, and operations (AECO) sector. Through the analysis of 36 BEP documents from international organizations, we have identified crucial components and put forth a comprehensive framework with the objective of improving digital transformation and collaboration in intricate construction projects. This study utilizes scientometric analysis to chart the development of BEP standards and incorporates empirical data from industry surveys to verify the suggested framework. The results of our research emphasize the advantages of using standardized building execution plans (BEPs) to decrease inefficiencies and enhance project outcomes. This makes a substantial contribution to the field of building information modeling (BIM) implementation.

Low Back Pain Risk Among Athlete: A Scientometric Study

Low back pain (LBP) emerges as a prevalent concern within the athletic community, characterized by a noteworthy proclivity for recurrence. The repeated occurrence of LBP substantially impedes athletes and adversely affects their performance. As such, it becomes imperative to discern the underlying factors contributing to injuries in the lumbar region. The scientometric analysis will provide a clear pathway for researchers to understand the current state and evolution of the theme research area of low back pain risk among athletes. No scientometric study on this theme has been undertaken until today. Therefore, this research objective was to collect data from the database to identify the present trend of low back pain risk, development patterns, and current research. Software that has been utilized for commonness, co-occurrence, clustering, co-citation and analysis of burst was CiteSpace. A scientometric review of studies on athletes' low back pain risk from 2000 to 2023 finds considerable additional studies in the amount of literature, the citation frequency, and the hotspots available. The most influential author is Douglas W. Jackson, the most influential article is Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study. Thoraco-lumbar spine is the most common cluster, whereas the most popular keyword is spondylolisthesis. In conclusion, low back pain is becoming one of the future study topics. Furthermore, many stakeholders must be aware of current trends and advances in the athlete's low back pain risk.

Authorship pattern and Degree of collaboration in the field of Diabetes Research output in India: A Scientometric analysis

Diabetes is one of the most prevalent diseases in India and has been a major cause of concern for many years. It is important to measure the author productivity of diabetes research in India. This paper will explore the trends in author productivity related to diabetes research in India over the past few years. This study focused on analyzing Diabetic research published via Web of Science (2018-2020). The results show that a total of 9185 papers were published. The year 2017 had the greatest number of publications, with 745 total documents. The analysis reveals that Journal articles constituted 8577 (93.381%) of total documents of source, followed by Website 406 (4.420%), Books 151 (1.644%), and respectively. Among all authors, V Mohan has published 164 outputs from Madras Diabetes Research Foundation, Chennai, followed by A Misra with 89 outputs in the field of diabetes research. The degree of collaboration is found to be 0.918 from 1938 to 2020 and found multiauthor dominating. The result of this study found that Indian authors contributed more articles on diabetes research and should be encouraged to contribute further works in this field.

Coral restoration: a mapping review through a scientometric analysis

Reef restoration has gained attention as it has strategic actions and powerful means in sustaining and maintaining coastal ecosystem services. This scientometric study systematically analyzes the current trends and research hotspot in coral restoration across the last five decades (1971–2022). The metadata (12,667 articles with 652,860 cited references) were obtained from the Clarivate Web of Science platform through the Core Collection database, associated with the CiteSpace and R‐software for further analysis. The results indicated that the trend of coral restoration is increasing in paper/year, with the United States, Australia, and China as major contributors to the related research. Furthermore, James Cook University, Australia had the largest number of articles, and the Consortium of Research Libraries of the United Kingdom was the most influential institution on coral restoration. The highly cited keywords are “Great Barrier Reef,” “climate change,” and “coral reef,” while the most influential keywords are “coral,” “model,” and “Atlantic.” A total of 23 clusters in the field with “coral reef,” “phase shift,” “bacterial communities,” “coral restoration,” “symbiotic dinoflagellate,” “stony coral tissue loss disease,” “bleaching event,” “ocean acidification,” “oyster reef,” and “quantitative reconstruction” are among the top cluster size labeled. Early‐stage researcher may use both keywords and cluster analysis to find topics attractive to their future research projects. In addition, this study contributes toward evaluating recent scientific productivity about coral restoration as well as informing researchers and policymakers regarding funding, future strategic planning, and potential collaboration opportunities.

Mapping global value chain, production network, and commodity chain literature using scientometrics: an integrative resilient value chains model based on a review of the literature

PurposeGlobal value chains (GVCs) are facing unprecedented pressures arising from structural changes in the global economy and exogenous shocks including military conflicts and the aftermath of COVID-19. Considering the importance of value chain analysis in the current environment, the purpose of the study is to provide an up-to-date overarching global value chain literature review study that offers suggestions for research and practice to ensure resilient value and supply chains.Design/methodology/approachThe authors provide a comprehensive review of literature of the value chain, commodity chain and production network research based on a systems overview of 5,628 publications to identify the extent of research on vulnerabilities and resilience of value chains globally and gaps therein. To provide the systems overview, the authors use scientometric content co-occurrence analysis methods to analyze and identify gaps within the existing literature.FindingsBased on this overarching review of the literature, the authors identify gaps in the literature primarily related to the issue of unpreparedness of value chains to exogenous shocks. The authors suggest future research directions and propose an integrative model along with recommendations for restructuring value chains for resilience amidst exogenous shocks.Originality/valueThis study carries out an overarching study of interdisciplinary GVC literature in the age of geopolitical and societal challenges and is thus able to offer holistic insights and propositions for future research.

Recent Trends of Customer Relationship Management in AI: A Scientometric Analysis

Theoretical background: The popularity of AI in recent years has led to its integration with various industrial sectors. This spectrum of AI applications has brought numerous topics that are underexplored and need to be taken up. One such area is the integration of AI with customer relationship management (CRM) systems. In consequence, this research is valuable for the scientific community. Purpose of the article: The purpose of the research is to conduct scientometric analysis in the field of AI in CRM and to identify the most crucial areas of research, identify motor and niche themes in the AI-CRM and indicate future research trends. Research methods: The methodology was divided into three parts: data collection, descriptive analysis, scientometric analysis. In the research, “R programming” and “Biblioshiny” were used to conduct scientometric analysis. This made it possible to generate charts and a table based on gathered data and then analyse the results. Main findings: The results show there is a growth in AI-CRM systems subject since 2019. Keywords like “CRM”, “public relationship” or “data mining” were often used in research articles. Future research trends can be define among others: acceptance and trust for AI powered technology, impact on companies’ environment or relationship with customer.

Innovations in safety management for construction sites: the role of deep learning and computer vision techniques

Purpose The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry. Design/methodology/approach The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area. Findings The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites. Originality/value This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.

Metabolic reprogramming in hepatocellular carcinoma: a bibliometric and visualized study from 2011 to 2023.

Metabolic reprogramming has been found to be a typical feature of tumors. Hepatocellular carcinoma (HCC), a cancer with high morbidity and mortality, has been extensively studied for its metabolic reprogramming-related mechanisms. Our study aims to identify the hotspots and frontiers of metabolic reprogramming research in HCC and to provide guidance for future scientific research and decision-making in HCC metabolism. Relevant studies on the metabolic reprogramming of HCC were derived from the Web of Science Core Collection (WoSCC) database up until November 2023. The bibliometrix tools in R were used for scientometric analysis and visualization. From 2011 to 2023, a total of 575 publications were obtained from WoSCC that met the established criteria. These publications involved 3,904 researchers and 948 organizations in 37 countries, with an average annual growth rate of 39.11% in research. These studies were published in 233 journals, with Cancers (n = 29) ranking first, followed by Frontiers in Oncology (n = 20) and International Journal of Molecular Sciences (n = 19). The top ten journals accounted for 26% of the 575 studies. The most prolific authors were Wang J (n = 14), Li Y (n = 12), and Liu J (n = 12). The country with the most publications is China, followed by the United States, Italy, and France. Fudan University had the largest percentage of research results with 15.48% (n = 89). Ally A's paper in Cell has the most citations. A total of 1,204 keywords were analyzed, with the trend themes such as "glycolysis," "tumor microenvironment," "Warburg effect," "mitochondria," "hypoxia ," etc. Co-occurrence network and cluster analysis revealed the relationships between keywords, authors, publications, and journals. Moreover, the close collaboration between countries in this field was elucidated. This bibliometric and visual analysis delves into studies related to metabolic reprogramming in HCC between 2012 and 2023, elucidating the characteristics of research in this field, which has gradually moved away from single glycolipid metabolism studies to the integration of overall metabolism in the body, pointing out the trend of research topics, and the dynamics of the interaction between the tumor microenvironment and metabolic reprogramming will be the future direction of research, which provides blueprints and inspirations for HCC prevention and treatment programs to the researchers in this field.

Global Trends in the Research and Development of Petrochemical Waste Gas from 1981 to 2022

As a highly energy-intensive and carbon-emitting industry with significant emissions of volatile organic compounds (VOCs), the petroleum and chemical industry is a major contributor to the global greenhouse effect and ozone layer destruction. Improper treatment of petrochemical waste gas (PWG) seriously harms human health and the natural environment. This study uses CiteSpace and VOSviewer to conduct a scientometric analysis of 1384 scholarly works on PWG and carbon sequestration published between 1981 and 2022, revealing the basic characteristics, knowledge base, research topic evolution, and research hotspots of the field. The results show the following: (1) In the early stages of the petrochemical industry, it was processed tail gas, plant leakage waste gas, and combustion flue gas that were investigated in PWG research. (2) Later, green environmental protection technology was widely studied in the field of PWG treatment, such as biotechnology, catalytic oxidation technology, membrane separation technology, etc., in order to achieve efficient, low energy consumption and low emissions of waste gas treatment, and the number of publications related to this topic has increased rapidly. In addition, researchers studied the internet of things and technology integration, such as the introduction of artificial intelligence, big data analysis, and other technologies, to improve the accuracy and efficiency of exhaust gas monitoring, control, and management. (3) The department has focused on how to reduce emissions by optimizing petrochemical process lines or improving energy efficiency. Emission reduction and low-carbon transition in the petrochemical industry will become the main trend in the future. Switching from renewable carbon to feedstock carbon derived from captured carbon dioxide, biomass, or recycled chemicals has become an attractive strategy to help curb emissions from the chemical industry. The results of our analysis can provide funding agencies and research groups with information to better understand the global trends and directions that have emerged in this field from 1981 to 2022 and serve as a reference for future research.

Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis

Crime is the intentional commission of an act usually suspected as socially detrimental and specifically defined, forbidden, and punishable under criminal law. Developing a society that is less susceptible to criminal acts makes crime prediction and pattern analysis (CPPA) a paramount topic for academic research interest. With the innovation in technology and rapid expansion of Artificial Intelligence (AI), the research in the field of CPPA has evolved radically to predict crime efficiently. While the number of publications is expanding substantially, we believe there is a dearth of thorough scientometric analysis for this topic. This work intends to analyze research conducted in the last decade using Scopus data and a scientometric technique, emphasizing citation trends and intriguing journals, nations, institutions, their collaborations, authors, and co-authorship networks in CPPA research. Furthermore, three field plots have been staged to visualize numerous associations between country, journal, keyword, and author. Besides, a comprehensive keyword analysis is carried out to visualize the CPPA research carried out with AI amalgamation. A total of five clusters have been identified depicting several AI methods used by the researchers in CPPA and the evolution of research trends over time from various perspectives.

The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This paper conducts a systematic review of the intelligent transportation literature using a scientometric analysis, aiming to summarize what is already known, identify current research trends, evaluate academic impacts, and suggest future research directions. The study provides a detailed review by analyzing 113 journal articles from the Web of Science (WoS) database. It examines the growth of publications over time, explores the collaboration patterns of key contributors, such as researchers, countries, and organizations, and employs techniques such as co-authorship analysis and keyword co-occurrence analysis to delve into the publication clusters and identify emerging research topics. Nine emerging sub-topics are identified and qualitatively discussed. The outcomes include recognizing pioneering researchers in intelligent transportation for potential collaboration opportunities, identifying reliable sources of information for publishing new work, and aiding researchers in selecting the best solutions for specific problems. These findings help researchers better understand the application of ML and DL in the intelligent transportation literature and guide research policymakers and editorial boards in selecting promising research topics for further research and development.

Scientometric evaluation of cutting‐edge research on sustainable development and environmental concern

AbstractSustainable development is an emerging field worldwide; given sustainable development, environmental concern is a significant and critical area. An attempt was made in this study to investigate the research areas and patterns associated with the growth of publication and citation growth related to sustainable development and environmental concerns. As a result of the download of 2258 publication details from the SCOPUS database, research data was downloaded and analyzed based on Vosviewer software by downloading and analyzing the research data. In the analysis of sustainable development and environmental concerns, scientometric analyses were conducted based on co‐authorship, keyword occurrence, bibliographic coupling, and co‐citation to visualize the results of the scientometric analysis.

Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

Scientometric Analysis of Activated Carbon or Probiotics in Mouthwashes or Toothpastes: Dynamicity, Spatiotemporal Evolution and Trends

The use of activated charcoal and probiotics is a controversial topic nowadays due to their potential oral health benefits. Thus, the aim of this research was to perform a scientometric analysis of activated charcoal or probiotics in mouthwashes or dentifrices by means of dynamicity, spatiotemporal evolution, and trends. A study was carried out to look back at scientific publications between 2005 and 2022 using the Web of Science. To analyze the data, various bibliometric indicators were used. The process of retrieving information was completed on July 28, 2023. It was found that only 1 article was published in 1990. Furthermore, the highest co-citation occurred in cluster 9 (Dentistry, Dermatology, Surgery), indicating a higher relevance and frequency with cluster 8 (Molecular Biology, Genetics). In the cluster view, 15 large clusters were identified, with cluster 0 (Activated Carbon) being the largest and occupying the greatest centrality. On the other hand, the cross-country collaboration map showed active collaborations between Australia and New Zealand, Brazil, and Canada. We found a significant growth of scientific publications on probiotics and activated charcoal in the field of dentistry and related disciplines between 1990 and 2023.

Bibliometric and scientometric analysis of PSMA-targeted radiotheranostics: knowledge mapping and global standing.

Bibliometric and scientometric analyses provide a structured approach to large amounts of data, enabling the prediction of research theme trends over time, the detection of shifts in the boundaries of disciplines, and the identification of the most productive countries, institutions and scholars. In the context of prostate-specific membrane antigen (PSMA)-targeted radiotheranostics, no bibliometric or scientometric analysis has been published thus far. Therefore, this study was conducted to identify key contributors to the literature, assess the global scientific production of related research, and possibly predict future development patterns. Scientometrics and bibliometrics were utilized to analyze the current body of knowledge while tracking its evolution to support scientific decision-making comprehensively and systematically. Science mapping techniques were employed to visualize research activities. Two different tools, Tableau and VOSviewer, were utilized, with VOSviewer being deemed the most suitable for the research objectives. The Web of Science (WoS) was used as the principal database for the searches. Through the search process over a period of 30 years (January 1993-January 2023), 694 original studies in the English language were subjected to comprehensive analysis. By employing bibliometric and scientometric methods, multiple networks were created that mapped various concepts, such as publication trends, leading countries, cocitations, coauthorship among researchers and scientists, as well as coauthorship among organizations and funding agencies. This study revealed the evolutionary patterns, trends, outliers, and key players in the PSMA field, which enabled a more nuanced understanding of the research landscape. This research contributes to the enrichment of knowledge on PSMA-targeted radiotheranostics through detailed global bibliometric and scientometric analyses. It stresses the necessity for the development of communication platforms, the establishment of supportive infrastructures, and the implementation of proactive solutions to address emerging challenges. This study offers a significant resource for delineating effective strategies and identifying prominent funding bodies essential for continuous advancements in the field of PSMA-based diagnosis and therapy for prostate cancer. It is vital to sustain this momentum to ensure further progress in this pioneering area.

Assessing the impact of history of medicine research: A scientometric and altmetric analysis.

After conducting a comprehensive literature search of two medical electronic databases, PubMed and Embase, as well as two citation databases, Web of Science Core Collections (WoS) and Scopus, we aimed to conduct an Altmetric and Scientometric analysis of the History of Medicine literature in medical research. The following software tools were used for analyzing the retrieved records from PubMed and Embase databases and conducting a collaboration analysis to identify the countries involved in scientific medical papers, as well as clustering keywords to reveal the trend of History of Medicine research for the future. These software tools (VOSviewer 1.6.18 and Spss 16) allowed the researchers to visualize bibliometric networks, perform statistical analysis, and identify patterns and trends in the data. Our analysis revealed 53,771 records from PubMed and 54,405 records from EMBASE databases retrieved in the field of History of Medicine by 105,286 contributed authors in WoS. We identified 157 countries that collaborated on scientific medical papers. By clustering 59,995 keywords, we were able to reveal the trend of History of Medicine research for the future. Our findings showed a positive association between traditional bibliometrics and social media metrics such as the Altmetric Attention Score in the History of Medicine literature (p < 0.05). Sharing research findings of articles in social scientific networks will increase the visibility of scientific works in History of Medicine research, which is one of the most important factors influencing the citation of articles. Additionally, our overview of the literature in the medical field allowed us to identify and examine gaps in the History of Medicine research.