A bibliometric analysis of top 100 most-cited articles in dentistry with author(s) affiliated with Taiwan institutes

Mapping out the bibliometric characteristics of classic articles published in a Taiwanese academic journal in dentistry: A scopus-based analysis

Background/purpose

Since its inception, the Journal of Dental Sciences (JDS) has aimed to publish quality articles relevant to all fields in dentistry. The purpose of this study was to analyze the bibliometric characteristics and dissected associated factors correlated with citation counts of classic articles published in the JDS.

Materials and method

Scopus® database was used to search the qualified articles published in JDS from 2009 to 2021. The bibliometric parameters, including journal impact factor (JIF), self-citation, study design, research field, geographic, country and institute of origin, inter-institute, inter-nation collaboration, keywords hotness and associated factors correlated with citation counts of classic articles were analyzed.

Results

One hundred and eight articles from Scopus® database were eligible for analysis. The citation counts of classic articles ranged from 12 to 192, the average citation was 22.02. The most common study design was the in vitro/in vivo, followed by the cross-sectional study, and the major research field were Dental Materials. The most productive country and institute is Taiwan, and Chung Shan Medical University, respectively. The trend of inter-institute (71.03%) and inter-nation (11.22%) collaboration steadily increased since 2009. By using the multivariable linear regression model, Preventive and Community Dentistry in the research field significantly increased the citation counts.

Conclusion

Despite its limitations, the escalating trends in JIFs, and JIFs without self-citations, and inter-nation and inter-institute collaboration of classic articles were noticed. Of all the dissected associated factors, Preventive and Community Dentistry in the research field significantly increased the citation counts of classic article.

The top 100 most-cited articles on adult spinal deformity: The most popular topics are still sagittal plane parameters and complications

Purpose

This study aimed to summarize the characteristics of the 100 most-cited articles on adult spinal deformity (ASD) and to analyze past and current research hotspots and trends.

Methods

Literature searches (from inception to 28 April 2022) using Web of Science databases were conducted to identify ASD-related articles. The top 100 most-cited articles were collected for further analysis. Meanwhile, author keywords from articles published in the last 5 years were selected for further analysis.

Results

The top 100 most-cited articles on ASD were selected from 3,354 papers. The publication year ranged from 1979 to 2017, and all papers were written in English. The citation count among them ranged from 100 to 1,145, and the mean citation number was 215.2. The foremost productive first author was Schwab F. University of Washington had the largest number of publications. The United States of America had the largest number of published articles (n = 84) in this field. Spine was the most popular journal. Complications were the most studied themes. The visualization analysis of author keywords from the literature in the recent 5 years showed that complications, sagittal plane parameters, and surgical techniques are still the research hotspots, and minimally invasive surgery will continue to develop rapidly.

Conclusion

Based on a comparative analysis of the results of bibliometric and visualization, complications and sagittal plane parameters are still the major topics of research at present and even later, and minimally invasive surgery has a growth trend in this field of ASD.

Research on artificial intelligence in shoulder and elbow surgery is increasing

Background

Total health care spending in the United States is increasing. In order to improve our delivery of high-quality, patient-centric, and cost-effective care, artificial intelligence (AI) and its subsets are being increasingly explored and utilized in medicine. Applications of AI in orthopedic surgery, including shoulder and elbow surgery, are being actively studied and have stirred much discussion. However, the trends of research on AI applications in shoulder and elbow surgery have not yet been quantified. Thus, the purpose of this study is to explore the general trends of research in applying AI to shoulder and elbow surgery and to examine characteristics of these research publications.

Methods

A literature search was conducted using PubMed for all articles published between January 1, 2000 and May 12, 2022. The primary search query used was as follows: (shoulder) and (AI OR machine learning OR deep learning OR neural networks). Exclusion criteria were as follows: (1) not pertinent to orthopedic surgeons (2) not pertaining to shoulder or elbow surgery, and (3) not pertaining to AI, machine learning, and deep learning. Selected articles in high-impact and relevant orthopedic journals were further characterized and analyzed.

Results

The annual number of articles increased from 1 article in 2006 to 24 articles in 2021. There was a 4-fold increase in publications between 2019 and 2021, and a 6-fold increase between 2018 and 2021. The average number of publications per year increased exponentially from 2010 to 2021 (R2 = 0.608; P = .003). The three journals with the most publications were Journal of Shoulder and Elbow Surgery (12), followed by Arthroscopy (2), and Clinical Orthopaedics and Related Research (2).

Conclusion

This study provides quantitative evidence for the first time that publications applying AI and its subsets to shoulder and elbow surgery are growing exponentially since the year 2010, with the most rapid growth beginning between the years of 2019 and 2020.