Bibliometric analysis of Oral and Maxillofacial Surgery publications

Abstract

Objective: The bibliometric analysis mapped the research in Bucomaxilofacial surgery with analysis of co-occurrence of terms. Methodology: A search was conducted in Pubmed, with the keyword "oral Surgery", between 1874 and 2022. The analysis and visualization of the data were made using the programming language R, with package Bibliometrix and software VOSviewer. We used a non-supervion machine learning algorithm, K-Means, to separate publications into clusters. The data were submitted to map analysis verifying the co-occurrence of terms and keywords. Graphic maps were generated that demonstrate the themes of the publications with the main keywords and the co-occurrence connection between them. Results: The algorithm separated the publications into 3 clusters, being agglomerated in three periods: 1) 1874 to 1999; 2) 2000 to 2013; 3) 2014 to 2022. In the network view, 5 clusters were identified in each period. The main words of each period were identified. The overlay visualization identified how recent the theme is and the density visualization which are the most researched themes. Conclusion: There has been an exponential increase in publications on the themes: maxillofacial surgery, dental implants, oral cancer, from 2014 and publications such as systematic reviews and meta-analyses from 2019.