Citation Rates Research Articles

Scientometric Mapping of Scientific Production on COVID-19 and the Practice of Exclusive Breastfeeding

Abstract Background: To determine the characteristics of scientific production related to exclusive breastfeeding during the coronavirus disease 2019 (COVID-19) pandemic. Materials and Methods: The Scopus database was used to extract metadata. Data on the number of papers and citations were extracted for countries, public and private institutions, journal metrics, most productive authors, and collaboration patterns. VOSviewer was used to visually represent the geographic distribution of global research. Results: A total of 1529 papers were obtained from the Scopus database, and 258 papers were excluded. Publications published in 2020 are mainly found in Q2. They also had higher national collaboration (38.90%) and citation rate per publication of 15.7. Among the universities that published scientific papers on the subject, the University of Milan had the highest number of scientific publications according to the top 10 institutions (n = 8), but University College London had a greater impact (n = 258). Similarly, for the review according to article production, the Journal of Human Lactation ranked first with a total of 19 publications. However, The Lancet, despite its position in the ninth place among the top ten countries, had higher citation potential and impact (13.1 and 179, respectively). Conclusions: According to the productivity index in the top ten categories of most productive authors, Marienelli stood out the most, unlike Mosca, who had a higher publication influence and citations per publication (219 and 43.8, respectively). Most of the publications on this subject were published in high-impact Q2 journals. Europe (Italy) has been the leading country in this field of research.

Family disruption, domestic violence and assaults against the police in Kentucky

PurposeInformed by the social disorganization theory (SDT), the study intends to identify the ecological factors most likely to predict assaults against the police recorded over five years in a southern American state, which has a relatively large rural population.Design/methodology/approachThe study uses aggregated county-level data on non-lethal assault against the police recorded in Kentucky between 2012 and 2016. We anticipated that structural conditions would affect directly and indirectly assaults against the police. Because predictors of social disorganization generally correlate with crimes police respond to, we hypothesized that the effects of SDT predictors on police victimization will be mediated by violent crime rates and domestic violence rates.FindingsThe study found partial empirical support for the social disorganization theory. Assaults against the police were more common in counties that had a higher proportion of female-headed households, higher rates of domestic violence citations and higher violent crime rates. Conversely, police victimization was less common in densely populated areas and in counties with higher poverty rates. While family disruption had significant direct and indirect positive effects on police victimization, the significant positive effect of ethnic heterogeneity (percent Black population) was only indirect. Residential instability did not predict significant variations in police victimization.Originality/valueTo our knowledge, no prior research attempted to determine if SDT is a valid theoretical framework when non-lethal assaults on police are examined beyond metropolitan areas. This analysis extends to counties research assessing the effects of structural conditions on nonfatal assaults against the police. Additionally, by including domestic violence among the correlates of police victimization, the current macro-level study addresses an apparent gap in the literature.

Patent-paper citations: A window into the technological impact of Tunisian research

This study constitutes the first report evaluating research performance at a national level by analyzing literature references cited in patents. The objective is to quantify the technological impact of the Tunisian research through patent-paper citations. Using the SciVal research analytics tool, we retrieved Tunisian publications cited in patents from 2013 to 2022. Our analysis of 83,681 scholarly outputs revealed that 1,121 documents were cited in 2,417 patents. These cited documents exhibited significantly higher citation metrics compared to the global average. Interestingly, documents not assigned to any quartile had been also frequently cited in patents, outperforming those from quartiles Q2 to Q4. These unassigned documents primarily consist of conference papers within domains such as Computer Science, Engineering, and Mathematics. Outputs with collaboration limited to national or institutional levels exhibited lower citation rates in patents compared to international collaboration. Conversely, academic-corporate collaborations exhibited the highest citation share at 5.2%. The distribution of patents citing Tunisian literature across 56 countries underscores the global significance of Tunisian scholarly outputs. Nevertheless, Tunisia's direct engagement in patenting activities remains very limited. Efforts to enhance Tunisia's participation in patenting activities are warranted to maximize the socio-economic benefits of its research endeavours.

Application of artificial intelligence to toxicological assessment of plant: a bibliometric analysis and future research plans

Artificial intelligence (AI) has gained attention in health science, with significant applications in the toxicological assessment of plants. However, a bibliometric analysis is essential to chart research trends and propose future advancements. This study explores key publications on AI’s role in plant toxicity assessment, identifying unresolved issues in pharmacological research. Articles from January 2008 to December 2023 were retrieved from the SCOPUS database, revealing a steady rise in publications, with a sharp increase from 2019 to 2023. A total of 75 research articles were analysed. The articles were categorised into four main clusters: AI applications, drug development, toxicity prediction models, and adverse drug event evaluations. ‘AI’ was the most frequently mentioned keyword, followed by ‘drug toxicity.’ Among 64 articles, the USA contributed 29, China 11, India 8, and the UK 4, with the UK having high citation rates. The findings highlight a growing trend toward AI-driven toxicity prediction in drug discovery. However, few studies provide definitive conclusions on AI’s potential in this field. The COVID-19 pandemic has heightened interest among researchers and policymakers. This study urges increased government and agency funding for AI-driven toxicity research. Advancing this field will enhance drug safety, reduce harmful testing, and promote sustainable plant use, aligning with SDGs 3, 12, and 15.

China’s policies and investments in metaverse and AI development: implications for academic research

Abstract Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

Mapping Hotspots and Emerging Trends in Global Wetlands Research: A Scientometric Analysis (2002–2022)

Recent studies have focused on wetlands due to their benefits for human spiritual satisfaction and mental health. This paper conducted a scientometric analysis of 2,388 studies published in the Web of Science database between 2002 and 2022. Using VOSviewer, Origin, and CiteSpace software, this study identified research hotspots and emerging trends in wetland research. The analysis revealed an upward trend in global wetland research publications, with notable contributions from researchers in the United States, China, Australia, Canada, and India. Network keyword co-occurrence analysis highlighted primary research themes, including constructed wetlands, climate change, wastewater treatment, phytoremediation, restoration, and hydrology. The United States emerged as the central hotspot for wetland research, with China, Canada, Australia, and other countries following. Given the growing recognition of wetlands' importance, wetland research is expected to gain even more global attention. Moreover, improvements in the quality of wetland tourism research are recommended, as most related publications have low citation rates. This paper provides a methodological overview of scientometric techniques applicable to global wetland research, offering scholars a framework for using scientometric analysis to enhance their future research. The increasing recognition of wetlands' crucial role in human well-being, encompassing both spiritual satisfaction and mental health, has led to a surge in research interest in this field. This study presents a comprehensive scientometric analysis of 2,388 wetland-related publications indexed in the Web of Science database between 2002 and 2022. Employing VOSviewer, Origin, and CiteSpace software, we mapped research hotspots and identified emerging trends within global wetland research. Our analysis reveals a significant upward trend in the volume of publications, highlighting the growing international attention to wetland ecosystems. The United States, China, Australia, Canada, and India have emerged as leading contributors to this research landscape. A network keyword co-occurrence analysis identified core research themes such as constructed wetlands, climate change, wastewater treatment, phytoremediation, ecological restoration, and hydrology. The United States is a central hub for wetland research, with China, Canada, and Australia also demonstrating substantial research activity. Given the escalating importance of wetlands in addressing global challenges, this research area is expected to attract further scholarly attention. We recommend a greater emphasis on enhancing the quality and impact of wetland tourism research, which currently exhibits low citation rates. Furthermore, this paper provides a methodological framework, demonstrating the application of scientometric techniques in global wetland research, thus empowering scholars to utilise such analytical approaches to refine their research. Our study offers a valuable and comprehensive overview of the key research areas, emerging topics, and influential contributors within the field of global wetland research.

The definition of highly cited researchers: the effect of different approaches on the empirical outcome

Abstract In 2001 onetime and since 2014 annually, Clarivate (and the former Thomson Reuters) has used publication and citation data to identify exceptional researchers—highly cited researchers (HCRs)—in nearly all disciplines. The approach used by Clarivate has not been without criticism. HCRs can be defined differently; the approach of Clarivate is one possibility among several others. HCRs can be identified by considering field-normalized citation rates or absolute numbers of citations; inclusion or exclusion of self-citations; full counting or fractional counting of publications; all authors, only corresponding authors or only first authors; short, long or varying citation windows; and short or long publication periods. In this study, we are interested in the effect different approaches have on the empirical outcomes. One may expect HCRs lists with large overlaps of authors, since all approaches are based on the same (bibliometric) data. As we demonstrated with five different variants of defining HCRs, the selection among these options has a significant influence on the sample of selected researchers and their characteristics that are thereby defined as highly cited. Some options have a stronger influence on the outcome than other options such as the length of the citation window or the focus on all authors versus only the corresponding author. Based on the empirical results of this study, we recommend that the user of HCR lists should always be aware of the influence these options have on the final lists of researchers.

Journal Quality Factors from ChatGPT: More meaningful than Impact Factors?

ABSTRACT Purpose Journal Impact Factors and other citation-based indicators are widely used and abused to help select journals to publish in or to estimate the value of a published article. Nevertheless, citation rates primarily reflect scholarly impact rather than other quality dimensions, including societal impact, originality, and rigour. In response to this deficit, Journal Quality Factors (JQFs) are defined and evaluated. These are average quality score estimates given to a journal’s articles by ChatGPT. Design/methodology/approach JQFs were compared with Polish, Norwegian and Finnish journal ranks and with journal citation rates for 1,300 journals with 130,000 articles from 2021 in large monodisciplinary journals in the 25 out of 27 Scopus broad fields of research for which it was possible. Outliers were also examined. Findings JQFs correlated positively and mostly strongly (median correlation: 0.641) with journal ranks in 24 out of the 25 broad fields examined, indicating a nearly science-wide ability for ChatGPT to estimate journal quality. Journal citation rates had similarly high correlations with national journal ranks, however, so JQFs are not a universally better indicator. An examination of journals with JQFs not matching their journal ranks suggested that abstract styles may affect the result, such as whether the societal contexts of research are mentioned. Research limitations Different journal rankings may have given different findings because there is no agreed meaning for journal quality. Practical implications The results suggest that JQFs are plausible as journal quality indicators in all fields and may be useful for the (few) research and evaluation contexts where journal quality is an acceptable proxy for article quality, and especially for fields like mathematics for which citations are not strong indicators of quality. Originality/value This is the first attempt to estimate academic journal value with a Large Language Model.

Social Media Mentions are a Stronger Predictor of Citations than 5-Year Impact Factor in Neurosurgical Research

BACKGROUND AND OBJECTIVES: X (formerly known as Twitter) is a social media platform gaining popularity in neurosurgery. Other disciplines have demonstrated a positive correlation between Twitter activity and traditional citation metrics. This study aims to determine if X activity is a greater predictor of citation rates than a journal's 5-year impact factor (IF) among major neurosurgical journals. METHODS: Using a mixed linear model, we compared the predictive value between alternative metrics (such as mentions on X and Altmetric attention score, a weighted aggregate of the attention an article receives on various platforms) and traditional citation metrics (5-year journal IF) on the number of citations an article received by analyzing 7592 articles published from January 2022 to December 2023 in 18 neurosurgical journals. It was necessary to also account for the confounding variable time since publication in the model to determine the true effect of altmetrics. The relative importance (RI) of each predictor variable was determined through permutation testing. RESULTS: X mentions, time since publication, 5-year journal IF, and Altmetric attention score all significantly predict citation rates (P < .001). RI of X mentions on citation rates indicate that X (RI = 0.13) is approximately 8.7x times greater of a predictor of citations than 5-year IF (RI = 0.015) and 5.4x times greater of a predictor than the Altmetric attention score (RI = 0.024). Time of publication remains the strongest predictor (RI = 0.83). CONCLUSION: Our study shows that in neurosurgical research, while social media mentions (X mentions) are significant, they are weaker predictors of citation rates than time since publication. Traditional journal IF and Altmetric attention scores have weaker predictive value. These findings indicate that altmetrics, especially X mentions, can complement traditional citation metrics.

Analysing a decade of <i>zakat</i> research through bibliometric analysis: Previous achievements, present patterns, and future avenues

Research on zakat has captured the attention of scholars since 1981, exhibiting an increasing trend in publications and citations. This trend presents an opportunity for the author to delve into zakat research. The primary aim of this study is to dissect 10 years of zakat research, spanning from 2013 to 2022, with a focus on evaluating past achievements, current research patterns, and potential future research directions. Utilising bibliometric analysis as the primary tool, this study has formulated seven research questions derived from the primary objective. Key findings indicate a consistent upward trajectory in both publication and citation rates over the past decade, with 2013 being a pivotal year. Notably, Malaysia, Indonesia, and Saudi Arabia emerged as the top three countries actively contributing to zakat research during this period. This study further outlines eight contemporary research trends, exploring various facets of zakat over the past decade. Additionally, this study identifies four prospective areas in zakat for future scholars to explore. This study’s outcomes offer three significant contributions: 1) signalling to scholars that zakat research continues to burgeon; 2) providing inspiration and ideas for current scholars; and 3) motivating future scholars to embark on research ventures in untapped areas within the realm of zakat.

Research Tools for Eating Behavior in People with Autism Spectrum Disorder (ASD).

To identify the tools used to assess eating behaviors in individuals with Autism Spectrum Disorder (ASD) and summarize their distribution, citation rates, journal publication, JCR scores, and psychometric properties. A literature review was conducted to identify studies on eating behavior in individuals with ASD. The search included various descriptors and combinations of keywords in databases such as Medline/PubMed, Science Direct, Scopus, SciELO, and Web of Science. The studies were filtered to focus on articles published in the last five years. Thirty-seven relevant studies were identified and analyzed to summarize the tools used, their distribution in the literature, citation rates, and psychometric properties. Thirty-seven relevant studies were identified. The Journal of Autism and Developmental Disorders published the most studies (5). The Brief Autism Mealtime Behavior Inventory (BAMBI) was the most frequently used instrument, appearing in 15 studies, followed by the Behavioral Pediatrics Feeding Assessment Scale (BPFAS) in 8 studies. Both instruments demonstrated solid psychometric properties, with BAMBI showing good internal consistency (α = 0.88) and BPFAS a Cronbach's alpha of 0.82. Most of the instruments used in studies on eating behavior in individuals with ASD demonstrate satisfactory psychometric properties. BAMBI and BPFAS stand out for their widespread use but are limited to covering only specific age ranges.

Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.

To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers. The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences. A total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were "model" and "human papillomavirus." The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates. By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.

The 100 most influential articles in mouth breathing. A bibliometric and altmetric analysis: 2002-2021.

This study aimed to identify and analyze the most influential Mouth Breathing (MB) articles in children and adolescents with the highest relative citation rates (RCRs), through bibliometric and altmetric analysis, from 2002 to 2021. On March 27, 2023 a PubMed search was conducted to detect papers published about MB. From a total of 826 documents, the article data were downloaded from iCite database. The 100 articles with the highest RCRs were selected for analysis in terms of RCR, citations, altmetric attention score, year, journal, first author (name, institution, country), subfield and design of study. The key words were analyzed using Vosviewer. Among the 100 articles, there were no differences between the two periods analyzed for RCR and AAS values, yet 2002-11 was more cited than 2012-2021. There was no correlation between RCR and AAS; but there was with citations. Brazil was the most cited country (n=30). The articles were published in 48 journals pertaining to 8 categories, 44 corresponding to Dentistry. The most frequent study design was the cross-sectional (n=48). Although all subfields were well represented, the most frequent ones were "treatment", "sleep disorders" and "clinical characteristics/cephalometry". The most prominent keywords were "malocclusion" and "cephalometry". Using RCR, a time- and field-normalized metric, one can identify influential articles in MB, a multidisciplinary research field of great importance for orthodontics. Because this bibliometric approach reduces the time from publication to the detection of an article´s importance for readers, it could be a valid alternative to using citation counts.

Deep learning-based object detection algorithms in medical imaging: Systematic review.

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.

Patterns of diversity in biomedical coauthorships: An analysis across authors' ethnicity, gender, age, and expertise.

Multiple studies have linked diversity in scientific collaborations to innovative and impactful research. Here, we explore how different diversity indices-ethnicity, gender, academic age, and topical expertise-interact and thereby influence scientific impact. Leveraging nearly 900,000 biomedical journal articles from PubMed, published in major journals between 1991 and 2014, we investigate the nuanced relationships among these diversity indices and their collective influence on research outcomes. By systematically varying model parametrizations, we assess the robustness of the observed relationships and examine multiple methodological choices. Our findings reveal a consistent pattern of demographic homophily, where scientists tend to collaborate with others who share similar ethnic and gender backgrounds. While each diversity index correlates significantly with impact when considered individually, gender diversity and topical expertise emerge as the strongest positive predictors of impact after accounting for key covariates. However, the association between diversity and impact is moderated by the number of collaborating authors, with larger teams sometimes showing opposite trends due to interactions between the computed diversity indices and team size. Despite this complexity, the practical drivers of scientific impact for an article remain the journal of publication, authors' prior citation rate, and the number of co-authors. On further examining expertise diversity through three separate dimensions: variety, balance, and disparity, our findings indicate that impactful teams balance a wide range of subject matter expertise while maintaining a focused connection on closely related topics. These findings highlight the importance of strategic team composition and underline the significance of team diversity in scientific research.

PUBLIC VALUE: A BIBLIOMETRIC REVIEW OF ARTICLES IN THE SCOPUS DATABASE

In the academic community, there is an increasing interest in the concept of public value, accompanied by efforts to define it by researchers from various fields. The aim of this article was to synthesize the scholarly work related to public value, published between 1994 and 2023. A bibliometric analysis of publications indexed in the Scopus database was conducted, following the selection of publications using specific inclusion criteria. The analysis covered scientific articles related to the concept of public value in the following areas: 1) social sciences, 2) business, management, and accounting, and 3) economics, econometrics, and finance, published worldwide in English. An analysis of the number of scholarly articles was performed, and authors with the highest number of articles, affiliated institutions, and scientific journals were identified. The frequency of international collaboration among authors (co-authorship) was determined. A frequency analysis of keywords was also conducted. Articles within the realm of public value with the highest citation rates were recognized. The data analysis process utilized R Studio software to support the bibliometric analysis

Analytic review of the first 15 years of journal functioning

This article reviews the activities of the journal Environmental Dynamics and Global Climate Change (EDGCC) over the 15 years since the publication of its first issue. The journal aims to inform readers about scientific and educational developments within the themes of "Environmental Dynamics" and "Global Climate Change." The main objectives of the journal include: Publishing papers, reviews and discussions addressing topics related to the composition, structure, and functioning of natural and anthropogenically altered systems under the climate change. Disseminating key findings of research across universities, academic institutions, and industry stakeholders. Fostering open scientific dialogue to improve the quality of research. Promoting national and international best practices in applying cutting-edge technologies. The journal accepts papers in both Russian and English. Submissions may include methodological, theoretical, and experimental works, ranging from regionally focused and federally funded projects to independent research yet to receive formal grant support. Recognizing the scarcity of high-quality Russian-language reviews in certain areas of global ecology and climatology, the journal also welcomes reviews and lectures by leading scientists to fill this gap. Papers undergo a double-blind peer review process, typically involving three reviewers who evaluate manuscripts anonymously without knowledge of the authors or their affiliations. This article presents scientometric data on the publication activity of EDGCC, along with an analysis of materials deemed most useful to readers. Building on a previous 10-year review, the article evaluates the journal's performance over the past five years. It highlights changes in publication format, particularly the shift to electronic-only articles, and their impact on key metrics. Papers with the highest reader engagement (measured by website views and citations) are identified. The analysis reveals that theoretical studies attract the greatest interest, followed closely by experimental works. Notably, a discussion paper achieved the fastest citation rate, while a "Chronicle" paper recorded the highest number of abstract views in the past five years. The journal's two-year impact factor has remained stable over the last five years, achieving competitive results compared to 27 peer journals with similar themes, often frequented by EDGCC's regular contributors. The number of authors publishing in EDGCC has remained consistent, averaging 16 authors annually, with approximately 50% being new contributors each year. A trend of increasing article half-life is observed over the past decade, indicating sustained interest in the journal's content. Additionally, the average h-index of EDGCC authors has shown an upward trend over time. In terms of "probability of citation after reading," EDGCC ranks third among the analyzed journals. This evaluation underscores EDGCC's enduring relevance and growing impact within the scientific community focused on environmental dynamics and climate change.

GLOBAL RESEARCH PATTERNS IN ORGAN DONATION: A BIBLIOMETRIC PERSPECTIVE

This bibliometric analysis examines global research trends in organ donation, using data from 813 articles extracted from the Scopus database. Organ donation remains a crucial area of study, involving ethical, cultural, and medical challenges that influence public policy, healthcare practices, and donor participation worldwide. However, despite its significance, there is limited understanding of how research on this topic is distributed globally, particularly regarding collaborative networks and keyword usage. This study addresses that gap by analyzing publication volume, keyword patterns, and country-based co-authorship. Using Scopus Analyzer and VOSviewer software, thematic clusters were identified, highlighting frequent keywords such as "ethics," "organ transplantation," and "informed consent," which collectively reveal a strong focus on ethical and procedural dimensions of organ donation. Analysis of co-authorship patterns revealed that the United States (US) leads in research output and international collaboration, followed by the United Kingdom (UK), Canada, and Germany, each contributing region-specific insights into ethical, cultural, and policy aspects. The high citation rates for countries like the US and the UK suggest these regions are central to shaping global discourse. Overall, the findings emphasize a need for greater global collaboration to address the diverse ethical and cultural contexts of organ donation, ultimately aiming to bridge gaps in donor availability and foster equitable healthcare policies worldwide. This bibliometric study offers a comprehensive overview of current research patterns, providing insights that may inform future studies and international initiatives in organ donation research.

Trends in Research on The Effects of Nutritional Supplements Against Nephrotoxicity; A Bibliometric Study

Aim; Publications investigating the potential of various nutritional supplements such as antioxidants, probiotics, and phytochemicals to improve drug-induced nephrotoxicity has increased both qualitatively and quantitatively over the years. This bibliometric analysis evaluated highly cited 100 articles on the protective effects of nutritional supplements against nephrotoxicity published between 2010 and 2023. Method; Articles published in these 13 years were evaluated through structured search in Scopus and Web of Science databases. Statistics and visualization techniques were done with VOSviewer and RStudio software. Results; The increase rate of articles published on this subject was found to be %450. The average h-index of selected studies was calculated as 60. A total of 534 authors and a citation rate of 86.95 were determined. The total citation mean was determined as 9 per year. The top 3 supplements were curcumin (n=7) melatonin and probiotics (n=5) in the trends. Cisplatin and gentamicin are used as toxic agents in 34% of the top 100 articles we researched, and they continue to be a trend. Conclusion; Trending and effective supplements in this field are curcumin, melatonin, berberine, quercetin and probiotics. Based on our investigations, we think that, the synergistic between the effective bioactive ingredients with probiotics and/or other functional foods suggest future novel approaches based on interactions involving microbiota, oxidative and inflammatory stress.

Has the UK lost its position as a destination for world-leading clinical research? A comparative analysis of haematological cancer clinical trials performance before Brexit

ObjectivesTo understand the competitive position of the UK in comparison to Europe and the USA for haematological cancer clinical research.DesignUsing commercially available databases, clinical trial numbers, their effectiveness and publication...