Characteristics and trends of the most cited papers in robotic assisted arthroplasty
Year: 2022
Type: article
Abstract: Purpose: To compile and analyze the top 50 most frequently cited articles published on robotic-assisted arthroplasty, allowing clinicians to effectively identify the most influential literature pertaining to this topic. Methods: Guidelines set by the Preferred Reporting Items for Systematic Reviews were used as the foundation for data collection and analysis. All papers pertaining to robotic-assisted arthroplasty were retrieved using the Scopus database. Data including: manuscript title, authors, total citation count, level of evidence, journal, publication year, and country of publication was extracted from a final list of articles. Results: The top 50 publications regarding robotic assisted arthroplasty were cited a total of 4530 times (including self-citations), with an average total of 91. The years with the most publications in the top 50 were: 2017, 2018, and 2019, producing 7, 6, and 5 papers, respectively. Only 4 papers in the top 50 were published prior to the year 2003. The most common level of evidence was level V, and the most common category was Clinical Outcomes (74%). The United States contributed half of the 50 articles, and Princess Grace Hospital and University of London were the most contributory institutions, each with 6 of the top 50 articles. Conclusion: This analysis provides a comprehensive review of the most cited and influential papers in robotic-assisted arthroplasty. Understanding these trends in the literature will ultimately pave the way for physicians and researchers to continue to innovate and research in a targeted manner as they gain an understanding of what has been studied and what remains inadequately explored. Level of evidence: 3.
Source: Journal of Orthopaedics
Institutions: University of Miami, University of Miami Hospital
Language: English
Subfield: Library and Information Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4290998817