A scientometric analysis of Scholarly output on COVID-19 and Dentistry

Impact of COVID-19 Dentistry-Related Literature: An Altmetric Study

INTRODUCTION

Scientific literature on COVID-19 has grown rapidly during the pandemic. The aim of this study was to provide a comprehensive overview of the popularity on the web of the available dental publications on COVID-19 and to examine associations amongst article characteristics, online mentions, and citations.

MATERIALS AND METHODS

An Altmetric Explorer search was conducted for COVID-19 articles published in dental journals using 3 keywords: COVID-19, SARS-CoV-2, and pandemic. The following Altmetric data were collected: Altmetric attention score (AAS), mentions by news outlets, tweets, Mendeley readers, and Web of Science citations. Additionally, article title, type, topic, origin and open access status, journal title, quartile of impact factor (IF) distribution, and time lapse between COVID-19 pandemic onset and publication date were analysed.

RESULTS

In all, 253 articles published in 48 dental journals were eligible for the study. AAS was significantly influenced by article topic, type, origin, and journal IF quartile. There was a negligible correlation between AAS and Web of Science citations. Mendeley was the only Altmetric source highly correlated with citations.

CONCLUSIONS

There was substantial online interest in COVID-19 dentistry-related literature, as depicted by the AAS of the reviewed articles and social media metrics. Mendeley reader counts were highly correlated with citations, and they may therefore be valuable in research impact evaluation.

Bibliometric analyses of applications of artificial intelligence on tuberculosis

Background

Tuberculosis is one of the leading causes of death worldwide affecting mainly low- and middle-income countries. Therefore, the objective is to analyze the bibliometric characteristics of the application of artificial intelligence (AI) in tuberculosis in Scopus.

Methods

A bibliometric study, the Scopus database was used using a search strategy composed of controlled and free terms regarding tuberculosis and AI. The search fields "TITLE," "ABSTRACT," and "AUTHKEY" were used to find the terms. The collected data were analyzed with Scival software. Bibliometric data were described through the figures and tables summarized by absolute values and percentages.

Results

Thousand and forty-one documents were collected and analyzed. Yudong Zhang was the author with the highest scientific production; however, K. C. Santosh had the greatest impact. Anna University (India) was the institution with the highest number of published papers. Most papers were published in the first quartile. The United States led the scientific production. Articles with international collaboration had the highest impact.

Conclusion

Articles related to tuberculosis and AI are mostly published in first quartile journals, which would reflect the need and interest worldwide. Although countries with a high incidence of new cases of tuberculosis are among the most productive, those with the highest reported drug resistance need greater support and collaboration.