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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Scientific Literature, Inc.
2020
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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. |
format | Online Article Text |
id | pubmed-7294844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | International Scientific Literature, Inc. |
record_format | MEDLINE/PubMed |
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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