Bibliometric analysis of laryngeal cancer treatment literature (2003-2023).

Despite advancements in medical science, the 5-year survival rate for laryngeal squamous cell carcinoma remains low, posing significant challenges in clinical management. This study explores the evolution of key topics and trends in laryngeal cancer research. Bibliometric and knowledge graph analysis are utilized to assess contributions in treating this carcinoma and to forecast emerging research hotspots that may enhance future clinical outcomes. The findings aim to guide researchers by identifying new areas, providing valuable insights and innovative perspectives. Data were extracted from the Web of Science Core Collection database on December 1, 2023. Bibliometric and knowledge mapping analyses were conducted using software tools such as R-Studio 4.1.3, CiteSpace 6.1.R6, VOSviewer 1.6.18, and http://bibliometre.com.(Both CiteSpace 6.1.R6 and VOSviewer 1.6.18 are widely used bibliometric analysis software tools, each with distinct features and applications. CiteSpace primarily focuses on analyzing literature citation relationships and generating knowledge graphs to visualize research hotspots, trends, and knowledge structures. Its data sources include platforms such as Web of Science. While CiteSpace excels in presenting knowledge structures through its advanced visualization capabilities, it is relatively complex to operate and less efficient in processing large-scale datasets. As a result, it is frequently employed in exploring research trends across multiple disciplines. On the other hand, VOSviewer is designed to construct various types of bibliometric networks and is characterized by its intuitive and user-friendly interface. It supports a wide range of data sources and produces visually appealing and clear visualizations, making it particularly suitable for multi-disciplinary bibliometric research. Additionally, VOSviewer provides valuable insights that can inform scientific research decision-making. Overall, the two tools differ in terms of functionality, data sources, visualization effects, and operational complexity, offering researchers complementary options for bibliometric analysis based on their specific needs.) From this database, 800 papers were extracted using specific criteria. After narrowing the scope to English-language publications, this number was reduced to 775. To ensure data quality, conference papers, letters, and editorial materials were excluded, focusing only on original research papers and review articles. The analysis showed that 760 theoretical works and review papers were published in 96 academic journals by 4210 authors from 1148 institutions across 60 countries/regions. The United States emerged as the most significant contributor to laryngeal cancer research. The Croatian Rudjer Boskovic Institute was notable for having the highest publication and citation counts. Among individual researchers, Osmak, M was identified as the most prolific and cited. Predominant international collaborations occurred between European and American countries. The Head and Neck Science Journal was the most frequently co-cited publication. Major research themes encompassed morphological aspects, chemotherapy, and molecular pathway mechanisms in laryngeal cancer treatment. Current research hotspots include disease prognosis, models, clinical trials, tumor recurrence, and surveillance. Notably, targeted therapy and immunotherapy are rapidly advancing fields. There is an urgent need to enhance global scholarly communication as the pursuit of effective laryngeal cancer treatment progresses. Focused research on targeting indicators for this type of cancer remains vital. An impending surge in research is driven by investigations into biomarkers, microenvironmental genetic mechanisms, alternatives to systemic chemotherapy, minimally invasive surgery, and herbal medicine explorations.

Zhao Y ，Xue J 《Heliyon》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》

Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.

The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are frequently not readily identifiable in their early stages. Once they have progressed to a more advanced stage, they impact the individual's quality of life and burden the family and broader society. In recent years, to solve this challenge well, the application of machine learning techniques in renal medicine has received much attention from researchers, and many results have been achieved in disease diagnosis and prediction. Nevertheless, studies that have conducted a comprehensive bibliometric analysis of the field have yet to be identified. This study employs bibliometric and visualization analyses to assess the progress of the application of machine learning in the renal field and to explore research trends and hotspots in the field. A search was conducted using the Web of Science Core Collection database, which yielded articles and review articles published from the database's inception to May 12, 2024. The data extracted from these articles and review articles were then analyzed. A bibliometric and visualization analysis was conducted using the VOSviewer, CiteSpace, and Bibliometric (R-Tool of R-Studio) software. 2,358 papers were retrieved and analyzed for this topic. From 2013 to 2024, the number of publications and the frequency of citations in the relevant research areas have exhibited a consistent and notable increase annually. The data set comprises 3734 institutions in 91 countries and territories, with 799 journals publishing the results. The total number of authors contributing to the data set is 14,396. China and the United States have the highest number of published papers, with 721 and 525 papers, respectively. Harvard University and the University of California System exert the most significant influence at the institutional level. Regarding authors, Cheungpasitporn, Wisit, and Thongprayoon Charat of the Mayo Clinic organization were the most prolific researchers, with 23 publications each. It is noteworthy that researcher Breiman I had the highest co-citation frequency. The journal with the most published papers was "Scientific Reports," while "PLoS One" had the highest co-citation frequency. In this field of machine learning applied to renal medicine, the article "A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury" by Tomasev N et al., published in NATURE in 2019, emerged as the most influential article with the highest co-citation frequency. A keyword and reference co-occurrence analysis reveals that current research trends and frontiers in nephrology are the management of patients with renal disease, prediction and diagnosis of renal disease, imaging of renal disease, and development of personalized treatment plans for patients with renal disease. "Acute kidney injury," "chronic kidney disease," and "kidney tumors" are the most discussed diseases in medical research. The field of renal medicine is witnessing a surge in the application of machine learning. On one hand, this study offers a novel perspective on applying machine learning techniques to kidney-related diseases based on bibliometric analysis. This analysis provides a comprehensive overview of the current status and emerging research areas in the field, as well as future trends and frontiers. Conversely, this study furnishes data on collaboration and exchange between countries, regions, institutions, journals, authors, keywords, and reference co-citations. This information can facilitate the advancement of future research endeavors, which aim to enhance interdisciplinary collaboration, optimize data sharing and quality, and further advance the application of machine learning in the renal field.

Li F ，Hu C ，Luo X 《-》

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling.

Elwenspoek MM ，Thom H ，Sheppard AL ，Keeney E ，O'Donnell R ，Jackson J ，Roadevin C ，Dawson S ，Lane D ，Stubbs J ，Everitt H ，Watson JC ，Hay AD ，Gillett P ，Robins G ，Jones HE ，Mallett S ，Whiting PF ... -  《-》

Exposure to hate in online and traditional media: A systematic review and meta-analysis of the impact of this exposure on individuals and communities.

People use social media platforms to chat, search, and share information, express their opinions, and connect with others. But these platforms also facilitate the posting of divisive, harmful, and hateful messages, targeting groups and individuals, based on their race, religion, gender, sexual orientation, or political views. Hate content is not only a problem on the Internet, but also on traditional media, especially in places where the Internet is not widely available or in rural areas. Despite growing awareness of the harms that exposure to hate can cause, especially to victims, there is no clear consensus in the literature on what specific impacts this exposure, as bystanders, produces on individuals, groups, and the population at large. Most of the existing research has focused on analyzing the content and the extent of the problem. More research in this area is needed to develop better intervention programs that are adapted to the current reality of hate. The objective of this review is to synthesize the empirical evidence on how media exposure to hate affects or is associated with various outcomes for individuals and groups. Searches covered the period up to December 2021 to assess the impact of exposure to hate. The searches were performed using search terms across 20 databases, 51 related websites, the Google search engine, as well as other systematic reviews and related papers. This review included any correlational, experimental, and quasi-experimental study that establishes an impact relationship and/or association between exposure to hate in online and traditional media and the resulting consequences on individuals or groups. Fifty-five studies analyzing 101 effect sizes, classified into 43 different outcomes, were identified after the screening process. Initially, effect sizes were calculated based on the type of design and the statistics used in the studies, and then transformed into standardized mean differences. Each outcome was classified following an exhaustive review of the operational constructs present in the studies. These outcomes were grouped into five major dimensions: attitudinal changes, intergroup dynamics, interpersonal behaviors, political beliefs, and psychological effects. When two or more outcomes from the studies addressed the same construct, they were synthesized together. A separate meta-analysis was conducted for each identified outcome from different samples. Additionally, experimental and quasi-experimental studies were synthesized separately from correlational studies. Twenty-four meta-analyses were performed using a random effects model, and meta-regressions and moderator analyses were conducted to explore factors influencing effect size estimates. The 55 studies included in this systematic review were published between 1996 and 2021, with most of them published since 2015. They include 25 correlational studies, and 22 randomized and 8 non-randomized experimental studies. Most of these studies provide data extracted from individuals (e.g., self-report); however, this review includes 6 studies that are based on quantitative analysis of comments or posts, or their relationship to specific geographic areas. Correlational studies encompass sample sizes ranging from 101 to 6829 participants, while experimental and quasi-experimental studies involve participant numbers between 69 and 1112. In most cases, the exposure to hate content occurred online or within social media contexts (37 studies), while only 8 studies reported such exposure in traditional media platforms. In the remaining studies, the exposure to hate content was delivered through political propaganda, primarily associated with extreme right-wing groups. No studies were removed from the systematic review due to quality assessment. In the experimental studies, participants demonstrated high adherence to the experimental conditions and thus contributed significantly to most of the results. The correlational and quasi-experimental studies used consistent, valid, and reliable instruments to measure exposure and outcomes derived from well-defined variables. As with the experimental studies, the results from the correlation and quasi-experimental studies were complete. Meta-analyses related to four dimensions were performed: Attitudinal changes, Intergroup dynamics, Interpersonal behaviors, and Psychological effects. We were unable to conduct a meta-analysis for the "Political Beliefs" dimension due to an insufficient number of studies. In terms of attitude changes, exposure to hate leads to negative attitudes (d Ex = 0.414; 95% confidence interval [CI] = 0.005, 0.824; p < 0.05; n = 8 and d corr = 0.322; 95% CI = 0.14, 0.504; p < 0.01; n = 2) and negative stereotypes (d Ex = 0.28; 95% CI = -0.018, 0.586; p < 0.10; n = 9) about individuals or groups with protected characteristics, while also hindering the promotion of positive attitudes toward them (d exp = -0.227; 95% CI = -0.466, 0.011; p < 0.10; n = 3). However, it does not increase support for hate content or political violence. Concerning intergroup dynamics, exposure to hate reduces intergroup trust (d exp = -0.308; 95% CI = -0.559, -0.058; p < 0.05; n = 2), especially between targeted groups and the general population, but has no significant impact on the perception of discrimination among minorities. In the context of Interpersonal behaviors, the meta-analyses confirm a strong association between exposure to hate and victimization (d corr = 0.721; 95% CI = 0.472, 0.97; p < 0.01; n = 3) and moderate effects on online hate speech perpetration (d corr = 0.36; 95% CI = -0.028, 0.754; p < 0.10; n = 2) and offline violent behavior (d corr = 0.47; 95%CI = 0.328, 0.612; p < 0.01; n = 2). Exposure to online hate also fuels more hate in online comments (d = 0.51; 95% CI = 0.034-0.984; p < 0.05; n = 2) but does not seem to affect hate crimes directly. However, there is no evidence that exposure to hate fosters resistance behaviors among individuals who are frequently subjected to it (e.g. the intention to counter-argue factually). In terms of psychological consequences, this review demonstrates that exposure to hate content negatively affects individuals' psychological well-being. Experimental studies indicate a large and significant effect size concerning the development of depressive symptoms due to exposure (d exp = 1.105; 95% CI = 0.797, 1.423; p < 0.01; n = 2). Additionally, a small effect size is observed concerning the link between exposure and reduced life satisfaction(d corr = -0.186; 95% CI = -0.279, -0.093; p < 0.01; n = 3), as well as increased social fear regarding the likelihood of a terrorist attack (d corr = -0.206; 95% CI = 0.147, 0.264; p < 0.01 n = 5). Conversely, exposure to hate speech does not seem to generate or be linked to the development of negative emotions related to its content. This systematic review confirms that exposure to hate in online and in traditional media has a significant negative impact on individuals and groups. It emphasizes the importance of taking these findings into account for policymaking, prevention, and intervention strategies. Hate speech spreads through biased commentary and perceptions, normalizing prejudice and causing harm. This not only leads to violence, victimization, and perpetration of hate speech but also contributes to a broader climate of hostility. Conversely, this research suggests that people exposed to this type of content do not show increased shock or revulsion toward it. This may explain why it is easily disseminated and often perceived as harmless, leading some to oppose its regulation. Focusing efforts solely on content control may then have a limited impact in driving substantial change. More research is needed to explore these variables, as well as the relationship between hate speech and political beliefs and the connection to violent extremism. Indeed, we know very little about how exposure to hate influences political and extremist views.

Madriaza P ，Hassan G ，Brouillette-Alarie S ，Mounchingam AN ，Durocher-Corfa L ，Borokhovski E ，Pickup D ，Paillé S ... -  《-》