Race and ethnic disparities arthroplasty trends and hotspots: Bibliometric analysis.

Introduction: Despite continual advancements in total joint arthroplasty and perioperative optimization, there remains national variability in outcomes. These outcome variabilities have been in part attributed to racial and ethnic disparities in healthcare quality and access to care. This study aims to identify arthroplasty racial and ethnic disparities research and to predict future hotspots. Methods: Ethnic and racial disparities articles between 1992 and 2022 were queried from the Web of Science Core Collection of Clarivate Analytics. Bibliometric indicators in excel format were extracted and subsequently imported for further analysis. Bibliometrix and VOSviewer analyzed current and previous research. Results: Database search yielded 234 total articles assessing racial and ethnic disparities between 1992 and 2022. Twenty-six countries published manuscripts with the United States producing the majority of publications. The Veterans Health Administration and University of Pittsburgh were the most relevant institutions. Ibrahim SA was the most relevant and influential author within this field. Visuals of thematic map and co-occurrences identified the basic, motor, and niche themes within the literature. Conclusions: Racial and ethnic disparity within arthroplasty literature demonstrate growing traction with global contributions. United States authors and institutions are the largest contributors within this field. This bibliometric analysis identified previous, current, and future trends for prediction of future hotspots.

Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years.

Background: Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms. Methods: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature. Results: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases. Conclusions: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

The utilization of US-based large data in arthroplasty research: Bibiliometric analysis of trends and hotspots.

Background: The accessibility of digital information has expanded orthopaedic surgery with expanded role of Big Databases. The increasing interest have led to creation of large databases with increasing utilization in retrospective studies. The aim of this study is to identify Big Database research and predict future hotspots. Methods: Big Database publications between 1982 and 2022 were identified from the Web of Science Core Collection of Clarivate Analytics. Bibliometric indicators were obtained and imported for further analysis with VOSviewer and Bibliometrix to identify previous and ongoing trends within this field. Results: Bibliometric sourcing identified 811 total articles that was associated with major databases. Twenty-eight countries published manuscript in the field with the United States as the largest contributor. The most relevant institutions were Cleveland Clinic and Harvard University. Mont MA was the most productive and influential author. Co-occurrence visualization and thematic map identified niche and major themes within the literature. Conclusions: Large Database research continue to show an increasing trend since 2011 with contributions globally. United States institutions and authors are the leading contributors in big database research. This study identifies previous, current, and developing trends within this field for future hotspot development.