An overview of the 35 years of research in the oral radiology: a bibliometric analysis

Bibliometric analysis: A few suggestions (Part Two)

This manuscript serves as a follow-up to our previous work, "bibliometric analysis: a few suggestions," where we highlighted key aspects of bibliometric research and offered recommendations for improving its rigor. In this draft, we expand on those ideas and provide additional suggestions aimed at enhancing the depth and scope of bibliometric analysis. By analyzing a few randomly selected bibliometric studies, we identified common practices and gaps in the field, which informed the inclusion of new recommendations not covered in our earlier paper. These suggestions include refining search string definitions, strategies for search field selection, performance indicators for authors, methods for studying thematic evolution, and a few techniques like bibliographic coupling, co-citation analysis, and three-way plots. This manuscript aims to improve the comprehensiveness and effectiveness of bibliometric research practices and contribute to the development of more robust methodologies in this field.

Research trends and mapping knowledge for maxillary sinus augmentation in oral and maxillofacial surgery

BACKGROUND

The method most frequently employed to alleviate the deficiency of vertical bone volume in the maxillary posterior region is maxillary sinus augmentation. Given its importance in the field of dental implants and its possible impact on implant survival, it is crucial to assess the caliber and bibliometric characteristics of research related to maxillary sinus augmentation. This study aimed to understand the development and trends of studies and guide future studies by performing a bibliometric analysis of research related to maxillary sinus augmentation.

METHODS

The Web of Science database was searched on 29.05.2024 with the developed search strategy. Articles were listed in descending order according to the number of citations and analyzed using the Bibliometrix program prepared in R base. The articles were analyzed in four sections: performance analysis, word analysis, collaboration analysis, and thematic analysis.

RESULTS

Wang HL is the most frequently listed author on this topic with 24 h-indexes and 1686 total citations. The highest number of articles were published in 2017, with 102 articles with an average of 2.7 citations. The most popular keyword was "dental implants." with 426 usages. Italy and the USA are the countries that have published the most collaborative papers. Clinical Oral Implants Research comes at the top when evaluating the journals that publish on maxillary sinus augmentation based on the h-index. Basic research and technical-oriented studies between 1999 and 2005 have given way to more clinical and patient-oriented research.

CONCLUSION

This bibliometric study will serve as a guide for future research by illustrating the existing state of knowledge and limitations regarding the development of sinus lifting methods, given the constantly growing demand for implant therapy.

Contemporary research trends on nanoparticles in endodontics: a bibliometric and scientometric analysis of the top 100 most-cited articles

Objectives

Advancements in nanotechnology have led to the widespread usage of nanoparticles in the endodontic field. This bibliometric study aimed to determine and analyze the top 100 most-cited articles about nanoparticles in endodontics from 2000 to 2022.

Materials and Methods

A detailed electronic search was conducted on the "Clarivate Analytics Web of Science, All Databases" to receive the most-cited articles related to the topic. Articles were ranked in descending order based on their citation counts, and the first 100 were selected for bibliometric analysis. Parameters such as citation density, publication year, journal, country, institution, author, study design, study field, evidence level, and keywords were analyzed.

Results

The top 100 most-cited articles received 4,698 citations (16-271) with 970.21 (1.91-181) citation density in total. Among decades, citations were significantly higher in 2011-2022 (p < 0.001). Journal of Endodontics had the largest number of publications. Canada and the University of Toronto made the highest contribution as country and institution, respectively. Anil Kishen was the 1 who participated in the largest number of articles. The majority of the articles were designed in vitro. The main study field was "antibacterial effect." Among keywords, "nanoparticles" followed by "Enterococcus faecalis" were used more frequently.

Conclusions

Developments in nanotechnology had an impact on the increasing number of studies in recent years. This bibliometric study provides a comprehensive view of nanoparticle advances and trends using citation analysis.

A bibliometric analysis of worldwide cancer research using machine learning methods

With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, "Support Vector Machine," "classification," and "deep learning" have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.