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The accessibility of academic literature has improved considerably because of the internet, with a range of platforms providing access online. It is now common for academic literature databases to use ranking algorithms to sort search results by ‘relevance’. However, it is often unclear how relevance is defined, and it varies across different platforms. This lack of transparency can potentially introduce bias, and impact the rigour of literature reviews. While there is a lack of clarity on the technical features of algorithms, online academic literature databases are now used extensively. There is a critical question of how those using the platforms perceive ranking to function in this context, and how they adapt their information-seeking behaviour. In this paper we present findings from a mixed-methods study, involving an online survey and in-depth interviews with academics, to understand their beliefs and assumptions about relevance ranking algorithms and their implications for academic practice.

 

 

Background: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, and its distant metastasis (PTCDM), although uncommon, seriously affects the survival rate and quality of life of patients. With the rapid development of science and technology, research in the field of PTCDM has accumulated rapidly, presenting a complex knowledge structure and development trend. Methods: In this study, bibliometric analysis was used to collect 479 PTCDM-related papers published between 2004 and 2023 through the Web of Science (WoS) Core Collection (WoSCC) database. Keyword clustering analysis was performed using VOSviewer and citespace, as well as dual-map overlay analysis, to explore knowledge flows and interconnections between different disciplines. Results: The analysis indicated that China, the United States, and South Korea were the most active countries in conducting research activities. Italy’s research was notable due to its higher average citation count. Keyword analysis revealed that “cancer,” “papillary thyroid carcinoma,” and “metastasis” were the most frequently used terms in these studies. The journal co-citation analysis underscored the dominant roles of molecular biology, immunology, and clinical medicine, as well as the growing importance of computer science in research. Conclusion: This study identified the main trends and scientific structure of PTCDM research, highlighting the importance of interdisciplinary approaches and the crucial role of top academic journals in promoting high-quality research. The findings not only provide valuable information for basic and clinical research on thyroid cancer but also offer guidance for future research directions.

 

 

The discipline of bibliometrics involves the application of mathematical and statistical methods to scholarly publications. The first attempts at systematic data collection were provided by Alfred Lotka and Samuel Bradford, who subsequently established the foundational laws of bibliometrics. Eugene Garfield ushered in the modern era of bibliometrics with the routine use of citation analysis and systematized processing. Key elements of bibliometric analysis include database coverage, consistency and accuracy of the data, data fields, search options, and analysis and use of metrics. A number of bibliometric applications are currently being used in medical science and health care. Bibliometric parameters and indexes may be increasingly used by grant funding sources as measures of research success. Universities may build benchmarking standards from bibliometric data to determine academic achievement through promotion and tenure guidelines in the future. This article reviews the history, definition, laws, and elements of bibliometric principles and provides examples of bibliometric applications to the broader health care community. To accomplish this, the Medline (1966–2014) and Web of Science (1945–2014) databases were searched to identify relevant articles; select articles were also cross-referenced. Articles selected were those that provided background, history, descriptive analysis, and application of bibliometric principles and metrics to medical science and health care. No attempt was made to cover all areas exhaustively; rather, key articles were chosen that illustrate bibliometric concepts and enhance the reader’s knowledge. It is important that faculty and researchers understand the limitations and appropriate uses of bibliometric data. Bibliometrics has considerable potential as a research area for health care scientists and practitioners that can be used to discover new information about academic trends, pharmacotherapy, disease, and broader health sciences trends.

 

 

We present a large-scale comparison of five multidisciplinary bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. The comparison considers scientific documents from the period 2008–2017 covered by these data sources. Scopus is compared in a pairwise manner with each of the other data sources. We first analyze differences between the data sources in the coverage of documents, focusing for instance on differences over time, differences per document type, and differences per discipline. We then study differences in the completeness and accuracy of citation links. Based on our analysis, we discuss the strengths and weaknesses of the different data sources. We emphasize the importance of combining a comprehensive coverage of the scientific literature with a flexible set of filters for making selections of the literature.

 

 

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

 

 

The purpose of this paper is to outline the current state of empirical research on banks’ risk reporting. In addition to the development of the research field over time, regulatory trends and drivers for academic research on risk reporting will be derived. The review follows a triangulated approach: In addition to a qualitative content analysis based on the SLR, a quantitative bibliometric analysis using the scientific visualization techniques bibliometrix and VOSviewer will serve as a robustness check. The sole focus of this SLR on banks reflects the regulatory specificity of the financial services industry and serves to derive recommendations for action for regulators, supervisors, and auditors. The article follows the tenor of macro- and micro-prudential banking regulation, which has raised market discipline to a new level through the implementation of regulatory disclosure standards in the context of the Basel III amendment, with a deliberate clustering of the research area according to risk types. By identifying research gaps and conceptualizing a research agenda, this paper continues to serve the academia to broaden the research field of risk disclosure, especially for banks.

 

 

Along with the development of productivity and efficiency analysis techniques, extensive research on the performance of hospitals has been conducted in the last few decades. In this article, we conduct a systematic review supported by a series of bibliometric analyses to obtain a panoramic perspective of the research about the productivity and efficiency of hospitals—a cornerstone of the healthcare system—with a focus on Australia and its peers, i.e., the UK, Canada, New Zealand, and Hong Kong. We focus on the bibliometric data in Scopus from 1970 to 2023 and provide a qualitative and critical analysis of major methods and findings in selected published journal articles.

 

 

The Metaverse is a rapidly developing technology that has attracted interest from various companies. Experts predict exponential growth in profits from the Metaverse market in the coming years. However, early stages of innovation often bring uncertain and speculative impressions, making its direction, timing, and future unclear. The viability of the Metaverse as either an innovation that fails or as the next technological revolution is questionable in its own right. Academically, the Metaverse has generated interest across various disciplines, including psychology, marketing, and legal studies. The aim of this study is to systematically consolidate the growing literature to understand the intricate dynamics of consumer behavior and marketing practices in this evolving sphere. To achieve this, a comprehensive bibliometric analysis was carried considering 284 contributions from the Web of Science on the Metaverse in business, management, marketing, and communication using quantitative analysis tools such as VOSviewer and SciMAT. The study provides insight into pioneering contributions, prominent authors, central themes, and emerging research challenges related to the Metaverse. The results contribute towards better understanding of the Metaverse, enabling well-informed decisions for efficient marketing strategies and future advancements in this ever-evolving realm for both practitioners and researchers.

 

 

Background: Radiation-induced brain injury (RIBI) is a debilitating sequela after cranial radiotherapy. Research on the topic of RIBI has gradually entered the public eye, with more innovations and applications of evidence-based research and biological mechanism research in the field of that. This was the first bibliometric analysis on RIBI, assessing brain injury related to radiation articles that were published during 1998–2023, to provide an emerging theoretical basis for the future development of RIBI. Methods: Literature were obtained from the Web of Science Core Collection (WOSCC) from its inception to December 31, 2023. The column of publications, author details, affiliated institutions and countries, publication year, and keywords were also recorded. Results: A total of 2543 journal articles were selected. The annual publications on RIBI fluctuated within a certain range. Journal of Neuro-oncology was the most published journal and Radiation Oncology was the most impactful one. LIMOLI CL was the most prolific author with 37 articles and shared the highest h-index with BARNETT GH. The top one country and institutions were the USA and the University of California System, respectively. Clusters analysis of co-keywords demonstrated that the temporal research trends in this field primarily focused on imaging examination and therapy for RIBI. Conclusion: This study collects, visualizes, and analyzes the literature within the field of RIBI over the last 25 years to map the development process, research frontiers and hotspots, and cutting-edge directions in clinical practice and mechanisms related to RIBI.

 

 

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.