Bibliometric Analysis of Comprehensive Geriatric Assessment from 2004 to 2023.

J Multidiscip Healthc

Department of Dermatology, Beijing Hospital, National Center of Gerontology, Beijing, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

Published: December 2024

Background: The global aging population necessitates specialized tools for complex geriatric health issues. Comprehensive Geriatric Assessment (CGA) provides multidimensional evaluations of elderly, integrating inputs from various professionals to create individualized care plans. This study aims to visually assess the research trends and hotspots in the field of CGA, review mainstream perspectives in this field, and provide a foundation for future research and treatment.

Methods: Original and review articles related to comprehensive geriatric assessment, published from 2004 to December 2023, were extracted from the Web of Science database. Four different software tools-CiteSpace, VOSviewer, Bibliometrix R package, and the Online Analysis Platform of Bibliometrics-were utilized for this comprehensive analysis.

Results: According to our retrieval strategy, we found a total of 4,411 related literatures. There has been a substantial increase in the research on comprehensive geriatric assessment in the past 20 years. These publications have been cited 157,366 times, with a mean of 35.68 citations per publication. The largest number of publications were from the US, and Italy ranked second (14.98%). Keyword burst and concurrence showed that "randomized trial", "adjuvant chemotherapy" and "breast cancer" were the top 3 most frequently occurring keywords.

Conclusion: Our bibliometric analysis reveals significant growth in CGA research over the past two decades, with a shift from cancer-focused studies to chronic conditions like frailty and sarcopenia. These findings highlight evolving priorities in geriatric care and underscore the need for future research to integrate technological advancements, such as AI, to enhance the precision, scalability, and cost-effectiveness of CGA in diverse settings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645894PMC
http://dx.doi.org/10.2147/JMDH.S488030DOI Listing

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