scholarly journals Using Kano diagrams to display the most cited article types, affiliated countries, authors and MeSH terms on spinal surgery in recent 12 years

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Po-Hsin Chou ◽  
Yu-Tsen Yeh ◽  
Wei-Chih Kan ◽  
Tsair-Wei Chien ◽  
Shu-Chun Kuo
Keyword(s):  
Impact Factor ◽  
Spinal Surgery ◽  
Scientific Productivity ◽  
The United States ◽  
Mesh Term ◽  
Medical Subject Headings ◽  
Productive Author ◽  
Mesh Terms ◽  
The Us ◽  
European Spine Journal

Abstract Background Citation analysis has been increasingly applied to assess the quantity and quality of scientific research in various fields worldwide. However, these analyses on spinal surgery do not provide visualization of results. This study aims (1) to evaluate the worldwide research citations and publications on spinal surgery and (2) to provide visual representations using Kano diagrams onto the research analysis for spinal surgeons and researchers. Methods Article abstracts published between 2007 and 2018 were downloaded from PubMed Central (PMC) in 5 journals, including Spine, European Spine Journal, The Spine Journal, Journal of Neurosurgery: Spine, and Journal of Spinal Disorders and Techniques. The article types, affiliated countries, authors, and Medical subject headings (MeSH terms) were analyzed by the number of article citations using x-index. Choropleth maps and Kano diagrams were applied to present these results. The trends of MeSH terms over the years were plotted and analyzed. Results A total of 18,808 publications were extracted from the PMC database, and 17,245 were affiliated to countries/areas. The 12-year impact factor for the five spine journals is 5.758. We observed that (1) the largest number of articles on spinal surgery was from North America (6417, 37.21%). Spine earns the highest x-index (= 82.96). Comparative Study has the highest x-index (= 66.74) among all article types. (2) The United States performed exceptionally in x-indexes (= 56.86 and 44.5) on both analyses done on the total 18,808 and the top 100 most cited articles, respectively. The most influential author whose x-index reaches 15.11 was Simon Dagenais from the US. (3) The most cited MeSH term with an x-index of 23.05 was surgery based on the top 100 most cited articles. The most cited article (PMID = 18164449) was written by Dagenais and his colleagues in 2008. The most productive author was Michael G. Fehlings, whose x-index and the author's impact factor are 13.57(= √(13.16*14)) and 9.86(= 331.57/33.64), respectively. Conclusions There was a rapidly increasing scientific productivity in the field of spinal surgery in the past 12 years. The US has extraordinary contributions to the publications. Furthermore, China and Japan have increasing numbers of publications on spinal surgery. This study with Kano diagrams provides an insight into the research for spinal surgeons and researchers.

scholarly journals Using Kano diagrams to display the most cited article types, affiliated countries, authors and MeSH terms on spinal surgery in recent 12 years

2020 ◽  
Author(s):  
Po-Hsin Chou ◽  
Yu-Tsen Yeh ◽  
Wei-Chih Kan ◽  
Chien Tsai Wei ◽  
Shu-Chun Kuo
Keyword(s):  
Impact Factor ◽  
Spinal Surgery ◽  
Scientific Productivity ◽  
The United States ◽  
Mesh Term ◽  
Medical Subject Headings ◽  
Productive Author ◽  
Mesh Terms ◽  
The Us ◽  
European Spine Journal

Abstract Background: Citation analysis has been increasingly applied to assess the quantity and quality of scientific research in various fields worldwide. However, these analyses on spinal surgery do not provide visualization of results. This study aims (1) to evaluate the worldwide research citations and publications on spinal surgery and (2) to provide visual representations using Kano diagrams onto the research analysis for spinal surgeons and researchers.Methods: Article abstracts published between 2007 and 2018 were downloaded from PubMed Central(PMC) in 5 journals, including Spine, European Spine Journal, The Spine Journal, Journal of Neurosurgery: Spine, and Journal of Spinal Disorders and Techniques. The article types, affiliated countries, authors, and Medical subject headings (MeSH terms) were analyzed by the number of article citations using x-index. Choropleth maps and Kano diagrams were applied to present these results. The trends of MeSH terms over the years were plotted and analyzed.Results: A total of 18,808 publications were extracted from the PMC database, and 17,245 were affiliated to countries/areas. The 12-year impact factor for the five spine journals is 5.758. We observed that (1) The largest number of articles on spinal surgery was from North America(6417, 37.21%). Spine earns the highest x-index(=82.96). Comparative Study has the highest x-index(=66.74) among all article types. (2) The United States performed exceptionally in x-indexes (=56.86 and 44.5) on both analyses done on the total 18,808 and the top 100 most cited articles, respectively. The most influential author whose x-index reaches 15.11 was Simon Dagenais from the US. (3) The most cited MeSH term with an x-index of 23.05 was surgery based on the top 100 most cited articles. The most cited article (PMID=18164449) was written by Dagenais and his colleagues in 2008. The most productive author was Michael G Fehlings, whose x-index and the author's impact factor are 13.57(=√(13.16*14)) and 9.86(=331.57/33.64), respectively.Conclusions: There was a rapidly increasing scientific productivity in the field of spinal surgery in the past 12 years. The US has extraordinary contributions to the publications. Furthermore, China and Japan have increasing numbers of publications on spinal surgery. This study with Kano diagrams provides an insight into the research for spinal surgeons and researchers.

Questions to the Article: A Global Overview of COVID-19 Research in the Pediatric Field (Preprint)

2021 ◽  
Author(s):  
CHIEN WEI ◽  
Julie Chi Chow ◽  
Willy Chou
Keyword(s):  
The United States ◽  
Mesh Term ◽  
Forest Plot ◽  
Medical Subject Headings ◽  
Mesh Terms ◽  
Pubmed Database ◽  
The Us ◽  
Choropleth Maps ◽  
The Difference ◽  
Choropleth Map

UNSTRUCTURED The article, published on 23 July 2021, is well-written and of interest, but remains several questions that are required for clarifications, such as (1) the static choropleth map of collaboration analysis between countries should be dynamically visualized and highlighted by top three countries on their publications and author collaboration characteristics; (2) the research achievements in authors, institutes, and countries should be quantified by author-weighted scheme considering author order in article bylines; and (3) keyword analysis was too simple to identify the difference in article types between countries. We downloaded 2,268 abstracts from the Pubmed database with a search string of (COVID-19[MeSH Major Topic]) AND (pediatrics[Affiliation]), similar to the mentioned study, and displayed (1) choropleth maps highlighted by the most productive and highly author-collaborated countries, and (2)forest plot to identify differences in article types between two countries. The medical subject headings(MeSH terms) were used to denote the article types in articles. We observed that (1) three top productive countries were the United States, Italy, and India; (2) three top countries collaborated the authors affiliated with the US were Canada, the United Kingdom, and Italy; and (3) only the MeSH term of epidemiology presents the difference in article types between the US and India when the top 10 most frequently occurred MeSH terms were compared. We produced the dashboard-type visualizations to provide valuable information for readers. The novel visual representations make data clear with a better understanding of bibliographic analysis. The methods used in this study are recommended for future studies, not just limited to the field of COVID-19 research.

scholarly journals The Trend of Scientific Productivity of Chinese, EuropeanUnion, and United States Universities and Private Companies: Does the Future Belong to E-Technology Companies?

Publications ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Mauro G. Carta ◽  
Matthias C. Angermeyer ◽  
Silvano Tagliagambe
Keyword(s):  
United States ◽  
European Union ◽  
Scientific Productivity ◽  
The United States ◽  
The European Union ◽  
Private Companies ◽  
American Universities ◽  
The Us ◽  
Parametric Statistics ◽  
The Eu

The purpose is to verify trends of scientific production from 2010 to 2020, considering the best universities of the United States, China, the European Union (EU), and private companies. The top 30 universities in 2020 in China, the EU, and the US and private companies were selected from the SCImago institutions ranking (SIR). The positions in 2020, 2015, and 2010 in SIR and three sub-indicators were analyzed by means of non-parametric statistics, taking into consideration the effect of time and group on rankings. American and European Union universities have lost positions to Chinese universities and even more to private companies, which have improved. In 2020, private companies have surpassed all other groups considering Innovation as a sub-indicator. The loss of leadership of European and partly American universities mainly concerns research linked to the production of patents. This can lead to future risks of monopoly that may elude public control and cause a possible loss of importance of research not linked to innovation.

The Most Cited Mesh Terms and Authors who Published Papers in Pubmed Central on the Topic of Medicine and Health Using Bibliometric Analyses

2019 ◽  
pp. 1-9
Author(s):  
Tsair-Wei Chien ◽  
Hing-Man Wu ◽  
Hsien-Yi Wang ◽  
Willy Chou
Keyword(s):  
Mesh Term ◽  
Deep Insight ◽  
Medical Subject Headings ◽  
Microsoft Excel ◽  
Pubmed Central ◽  
Current State ◽  
Mesh Terms ◽  
Control Gain ◽  
State Of Research ◽  
Insight Into

Aims: We visualized the current state of research on publication outputs and citations in the field of medicine and health to uncover topic burst and citations among medical subject headings (MeSH) clusters. Study Design: A bibliometric analysis. Place and duration of Study: Using Pubmed indexed articles to inspect the characteristics of topics on medicine and health since 1969. Methodology: Selecting 156 abstracts, author names, countries, and MeSH terms on January 10, 2019, from Pubmed Central (PMC) based on the terms of medicine and health in the title since 1969, we applied the x-index and impact factor to evaluate author individual research achievements and compute MeSH bibliometric performances. The bootstrapping method was used to estimate the median and its 95% confidence intervals and make differences in metrics among MeSH clusters. The dominant nations were selected using the x-index to display on a dashboard. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for displaying graphical representations. Results: We found that (1)the dominant countries/areas are the Unlited States, Taiwan, and Australia; (2) the author Grajales, Francisco Jose 3rd form Canada has the most cited metrics such as author IF=39.46 and x-index=6.28; (3)the MeSH terms of organization & administration, standards, and prevention & control gain the top three degree centralities among MeSH clusters; (4) No any differences in metrics were found among MeSH clusters; (5) the article(PMID= 24518354) with three MeSH term of delivery of health care, social media, and software and published in 2014 was cited most at least 62 times. Conclusion: Social network analysis provides wide and deep insight into the relationships among MeSH terms. The MeSH weighted scheme and x-index were recommended to academics for computing MeSH citations in the future.

scholarly journals Human Breathomics Database

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Tien-Chueh Kuo ◽  
Cheng-En Tan ◽  
San-Yuan Wang ◽  
Olivia A Lin ◽  
Bo-Han Su ◽  
...  
Keyword(s):  
Text Mining ◽  
Exhaled Breath Condensate ◽  
Mesh Term ◽  
Exhaled Breath ◽  
Medical Subject Headings ◽  
Mesh Terms ◽  
Disease Information ◽  
Volatile Organic ◽  
Breath Condensate ◽  
Automatic Pipeline

Abstract Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database—the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients. Database URL: https://hbdb.cmdm.tw

scholarly journals Using MeSH (Medical Subject Headings) to Enhance PubMed Search Strategies for Evidence-Based Practice in Physical Therapy

2012 ◽  
Vol 92 (1) ◽  
pp. 124-132 ◽  
Author(s):  
Randy R. Richter ◽  
Tricia M. Austin
Keyword(s):  
Search Strategy ◽  
Evidence Based Practice ◽  
Physical Therapists ◽  
Mesh Term ◽  
Evidence Based ◽  
Medical Subject Headings ◽  
Search Terms ◽  
Mesh Terms ◽  
Subject Headings ◽  
Pubmed Search

Background Evidence-based practice (EBP) is an important paradigm in health care. Physical therapists report lack of knowledge and time constraints as barriers to EBP. Objective The purpose of this technical report is to illustrate how Medical Subject Headings (MeSH), a controlled vocabulary thesaurus of indexing terms, is used to efficiently search MEDLINE, the largest component of PubMed. Using clinical questions, this report illustrates how search terms common to physical therapist practice do or do not map to appropriate MeSH terms. A PubMed search strategy that takes advantage of text words and MeSH terms is provided. Results A search of 139 terms and 13 acronyms was conducted to determine whether they appropriately mapped to a MeSH term. The search results were categorized into 1 of 5 outcomes. Nearly half (66/139) of the search terms mapped to an appropriate MeSH term (outcome 1). When a search term did not appropriately map to a MeSH term, it was entered into the MeSH database to search for an appropriate MeSH term. Twenty-one appropriate MeSH terms were found (outcomes 2 and 4), and there were 52 search terms for which an appropriate MeSH term was not found (outcomes 3 and 5). Nearly half of the acronyms did not map to an appropriate MeSH term, and an appropriate MeSH term was not found in the database. Limitations The results are based on a limited number of search terms and acronyms. Conclusions Understanding how search terms map to MeSH terms and using the PubMed search strategy can enable physical therapists to take full advantage of available MeSH terms and should result in more-efficient and better-informed searches.

The Role of Technology and Informatics in Disaster Planning and Response

2012 ◽  
Vol 30 (1) ◽  
pp. 149-168 ◽  
Author(s):  
Elizabeth Weiner ◽  
Lynn A. Slepski
Keyword(s):  
Disaster Planning ◽  
Mesh Term ◽  
Disaster Medicine ◽  
Medical Subject Headings ◽  
Humanitarian Response ◽  
Mesh Terms ◽  
Pubmed Search ◽  
Impact Of Technology ◽  
The Impact

It is clear that technology and informatics are becoming increasingly important in disasters and humanitarian response. Technology is a critical tool to recording, analyzing, and predicting trends in data that could not be achieved prior to its implementation. Informatics is the translation of this data into information, knowledge, and wisdom. Combining technology and informatics applications with response efforts has resulted in various enhanced biosurveillance efforts, advanced communications, and information management during disasters. Although these efforts have been well described in the literature, research on the impact of technology and informatics during these efforts has been limited. As a result, this chapter will provide an overview of these technology and informatics solutions and present suggestions for further research in an era when disaster and humanitarian response efforts continue to increase as well. A literature search was performed using PubMed search tools with the National Library of Medicine Medical Subject Headings (MeSH) terms of “disasters,” “disaster planning,” “disaster medicine,” “technology,” “informatics,” and “research.” Search limitations were set for 5 years and in English. Because of the limited number of research articles in this field, the MeSH term research was deleted.

scholarly journals Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation

2016 ◽  
Author(s):  
Neil R Smalheiser ◽  
Gary Bonifield
Keyword(s):  
Scientific Discovery ◽  
Semantic Relatedness ◽  
Mesh Term ◽  
The Body ◽  
Similarity Metrics ◽  
Name Disambiguation ◽  
Medical Subject Headings ◽  
Mesh Terms ◽  
Author Name Disambiguation ◽  
Subject Headings

In the present paper, we have created and characterized several similarity metrics for relating any two Medical Subject Headings (MeSH terms) to each other. The article-based metric measures the tendency of two MeSH terms to appear in the MEDLINE record of the same article. The author-based metric measures the tendency of two MeSH terms to appear in the body of articles written by the same individual (using the 2009 Author-ity author name disambiguation dataset as a gold standard). The two metrics are only modestly correlated with each other (r = 0.50), indicating that they capture different aspects of term usage. The article-based metric provides a measure of semantic relatedness, and MeSH term pairs that co-occur more often than expected by chance may reflect relations between the two terms. In contrast, the author metric is indicative of how individuals practice science, and may have value for author name disambiguation and studies of scientific discovery. We have calculated article metrics for all MeSH terms appearing in at least 25 articles in MEDLINE (as of 2014) and author metrics for MeSH terms published as of 2009. The dataset is freely available for download and can be queried at http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html.

scholarly journals A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed

2020 ◽  
Vol 13 ◽  
pp. 175628482093459
Author(s):  
Kangtao Wang ◽  
Chenzhe Feng ◽  
Ming Li ◽  
Qian Pei ◽  
Yuqiang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Basic Research ◽  
Mesh Term ◽  
Publication Date ◽  
Medical Subject Headings ◽  
The Past ◽  
Mesh Terms

Background and Aims: The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis. Methods: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term ‘Rectal Neoplasms’ from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles’ abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. Results: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. Conclusions: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.

scholarly journals Predicting the number of article citations in the field of attention-deficit/hyperactivity disorder (ADHD) with the 100 top-cited articles since 2014: a bibliometric analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chien-Ho Lin ◽  
Tsair-Wei Chien ◽  
Yu-Hua Yan
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Bibliometric Analysis ◽  
Attention Deficit ◽  
Neurodevelopmental Disorder ◽  
The United States ◽  
Child And Adolescent Psychiatry ◽  
Academic Disciplines ◽  
Medical Subject Headings ◽  
Hyperactivity Disorder ◽  
Mesh Terms

Abstract Background Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children or early adolescents with an estimated worldwide prevalence of 7.2%. Numerous articles related to ADHD have been published in the literature. However, which articles had ultimate influence is still unknown, and what factors affect the number of article citations remains unclear as well. This bibliometric analysis (1) visualizes the prominent entities with 1 picture using the top 100 most-cited articles, and (2) investigates whether medical subject headings (i.e., MeSH terms) can be used in predicting article citations. Methods By searching the PubMed Central® (PMC) database, the top 100 most-cited abstracts relevant to ADHD since 2014 were downloaded. Citation rank analysis was performed to compare the dominant roles of article types and topic categories using the pyramid plot. Social network analysis (SNA) was performed to highlight prominent entities for providing a quick look at the study result. The authors examined the MeSH prediction effect on article citations using its correlation coefficients (CC). Results The most frequent article types and topic categories were research support by institutes (56%) and epidemiology (28%). The most productive countries were the United States (42%), followed by the United Kingdom (13%), Germany (9%), and the Netherlands (9%). Most articles were published in the Journal of the American Academy of Child and Adolescent Psychiatry (15%) and JAMA Psychiatry (9%). MeSH terms were evident in prediction power on the number of article citations (correlation coefficient = 0.39; t = 4.1; n = 94; 6 articles were excluded because they do not have MeSH terms). Conclusions The breakthrough was made by developing 1 dashboard to display 100 top-cited articles on ADHD. MeSH terms can be used in predicting article citations on ADHD. These visualizations of the top 100 most-cited articles could be applied to future academic pursuits and other academic disciplines.

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