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04 June 2020 : Database Analysis  

Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018

Shuang Li1ABCEF, Hang Wang2ABCEF, Hong Zheng34BCD, Nana Li1BCD, Chao Sun34CD, Xingchu Meng34CD, Weiping Zheng34CD, Kai Wang34D, Hong Qin34D, Wei Gao34A*, Zhongyang Shen34G

DOI: 10.12659/MSM.922517

Med Sci Monit 2020; 26:e922517


BACKGROUND: Pediatric liver transplantation is used to treat children with end-stage liver disease. This study explored the research hotspots and bibliometric characteristics of pediatric liver transplantation through a variety of bibliometric analysis software. We conducted hotspot analysis to help determine important directions for future scientific research.

MATERIAL AND METHODS: The study samples were articles related to pediatric liver transplantation published in PubMed in the past 5 years. The high-frequency keywords are extracted by BICOMB software, and then a binary matrix and a common word matrix were constructed. Gcluto software was used to perform double-clustering and visual analysis on high-frequency words, and then we obtained hot area classification. Strategic coordinates are constructed using Excel. Citespace and VOSviewer software are used for further analysis and bibliometric data visualization.

RESULTS: A total of 36 high-frequency words were found in the 4118 studies. A peak map was drawn through double-cluster analysis. Biclustering analysis was used to calculate the concentricity and density of each hotspot. We obtained the top 10 countries/regions engaged in pediatric liver transplantation research. VOSviewer was used to visualize the co-author map.

CONCLUSIONS: We found 5 clusters and 7 aspects for pediatric liver transplantation. Additionally, calculation results showed that post-transplant lymphoproliferative disorder in pediatric patients and outcomes of multivisceral transplantation seem very promising. This conclusion is of great value for future exploratory research.

Keywords: Bibliometrics, Liver Transplantation, Pediatrics, Adolescent, Child, Child, Preschool, Cluster Analysis, Models, Statistical, PubMed, Publications, Software

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Dinah V. Parums

DOI: 10.12659/MSM.944927

Med Sci Monit 2024; 30:e944927

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750