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

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...

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Detalles Bibliográficos
Autores principales: Li, Shuang, Wang, Hang, Zheng, Hong, Li, Nana, Sun, Chao, Meng, Xingchu, Zheng, Weiping, Wang, Kai, Qin, Hong, Gao, Wei, Shen, Zhongyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294844/
https://www.ncbi.nlm.nih.gov/pubmed/32493895
http://dx.doi.org/10.12659/MSM.922517
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author Li, Shuang
Wang, Hang
Zheng, Hong
Li, Nana
Sun, Chao
Meng, Xingchu
Zheng, Weiping
Wang, Kai
Qin, Hong
Gao, Wei
Shen, Zhongyang
author_facet Li, Shuang
Wang, Hang
Zheng, Hong
Li, Nana
Sun, Chao
Meng, Xingchu
Zheng, Weiping
Wang, Kai
Qin, Hong
Gao, Wei
Shen, Zhongyang
author_sort Li, Shuang
collection PubMed
description 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/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.
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spelling pubmed-72948442020-06-22 Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018 Li, Shuang Wang, Hang Zheng, Hong Li, Nana Sun, Chao Meng, Xingchu Zheng, Weiping Wang, Kai Qin, Hong Gao, Wei Shen, Zhongyang Med Sci Monit Database Analysis 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/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. International Scientific Literature, Inc. 2020-06-04 /pmc/articles/PMC7294844/ /pubmed/32493895 http://dx.doi.org/10.12659/MSM.922517 Text en © Med Sci Monit, 2020 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Database Analysis
Li, Shuang
Wang, Hang
Zheng, Hong
Li, Nana
Sun, Chao
Meng, Xingchu
Zheng, Weiping
Wang, Kai
Qin, Hong
Gao, Wei
Shen, Zhongyang
Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title_full Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title_fullStr Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title_full_unstemmed Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title_short Bibliometric Analysis of Pediatric Liver Transplantation Research in PubMed from 2014 to 2018
title_sort bibliometric analysis of pediatric liver transplantation research in pubmed from 2014 to 2018
topic Database Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294844/
https://www.ncbi.nlm.nih.gov/pubmed/32493895
http://dx.doi.org/10.12659/MSM.922517
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