Dataset Information

Current status of global research on pericardial diseases: a bibliometric analysis of the top 100 from Web of Science.

ABSTRACT:

Introduction

Bibliometric studies can help guide researchers and funding bodies toward fields where more research activity is warranted. Bibliometric analyses have previously been published in many specialties and sub-specialties. Our literature search did not show a bibliometric analysis on pericardial diseases. We performed a bibliometric analysis of the top 100 cited manuscripts on pericardial diseases to identify knowledge.

Material and methods

Bibliometric analysis is a quantitative method to assess research performance and analyze publication trends. Web of Science was searched in April 2020 to identify the top 100 cited manuscripts in pericardial diseases.

Results

Twenty-six out of the top 100 cited manuscripts were published between 2000 and 2009. These manuscripts were cited on average189 times (range: 110-743) since publication. Only two manuscripts were cited > 500 times. Among the top-ten cited manuscripts, there were 6 original articles, 1 case series, and 3 review articles. Of the 3 review articles, 2 were society guidelines. 90% of the authors had written just 1 manuscript. There were ten manuscripts with women as first authors with a significant association between gender of the first and corresponding author (odds ratio = 44, p < 0.001). Only 20% of manuscripts were funded. Most publications came from institutions in the United States (n = 40), Italy (n = 10), and Spain (n = 5).

Conclusions

Our study provides an insight into the characteristics and quality of the highly cited literature in the field of pericardial diseases. This can be used to guide further research in the field of pericardial diseases.

SUBMITTER: Jain V

PROVIDER: S-EPMC10711781 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Current status of global research on pericardial diseases: a bibliometric analysis of the top 100 from Web of Science.

Jain Vardhmaan V Gupta Kartik K Eken Hatice Nur HN Bansal Agam A Khan Muhammad Shahzeb MS Bajaj Navkaranbir S NS Lavie Carl J CJ Imazio Massimo M Xu Bo B Kwon Deborah D Jellis Christine C Klein Allan A Aronow Wilbert S WS

Archives of medical sciences. Atherosclerotic diseases 20230727

<h4>Introduction</h4>Bibliometric studies can help guide researchers and funding bodies toward fields where more research activity is warranted. Bibliometric analyses have previously been published in many specialties and sub-specialties. Our literature search did not show a bibliometric analysis on pericardial diseases. We performed a bibliometric analysis of the top 100 cited manuscripts on pericardial diseases to identify knowledge.<h4>Material and methods</h4>Bibliometric analysis is a quant ...[more]

PMID: 38089161

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Project description:ObjectiveThe aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure-related machine learning publications.Materials and methodsWeb of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.ResultsThe search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.ConclusionsThis analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

Project description:Background: Bacterial persisters are thought to be responsible for the recalcitrance and relapse of persistent infections, and they also lead to antibiotic treatment failure in clinics. In recent years, researches on bacterial persisters have attracted worldwide attention and the number of related publications is increasing. The purpose of this study was to better understand research trends on bacterial persisters by identifying and bibliometrics analyzing the top 100 cited publications in this field. Methods: The Web of Science Core Collection was utilized to retrieve the highly cited publications on bacterial persisters, and these publications were cross-matched with Google Scholar and Scopus. The top 100 cited publications were identified after reviewing the full texts. The main information of each publication was extracted and analyzed using Excel, SPSS, and VOSviewer. Results: The top 100 cited papers on bacterial persisters were published between 1997 and 2019. The citation frequency of each publication ranged from 147 to 1815 for the Web of Science Core Collection, 153 to 1883 for Scopus, and 207 to 2,986 for Google Scholar. Among the top 100 cited list, there were 64 original articles, 35 review articles, and 1 editorial material. These papers were published in 51 journals, and the Journal of Bacteriology was the most productive journal with 8 papers. A total of 14 countries made contributions to the top 100 cited publications, and 64 publications were from the United States. 15 institutions have published two or more papers and nearly 87% of them were from the United States. Kim Lewis from Northeastern University was the most influential author with 18 publications. Furthermore, keywords co-occurrence suggested that the main topics on bacterial persisters were mechanisms of persister formation or re-growth. Finally, “Microbiology” was the most frequent category in this field. Conclusion: This study identified and analyzed the top 100 cited publications related to bacterial persisters. The results provided a general overview of bacterial persisters and might help researchers to better understand the classic studies, historical developments, and new findings in this field, thus providing ideas for further research.

Dataset Information

Current status of global research on pericardial diseases: a bibliometric analysis of the top 100 from Web of Science.

Introduction

Material and methods

Results

Conclusions

Publications

Current status of global research on pericardial diseases: a bibliometric analysis of the top 100 from Web of Science.

Similar Datasets

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