Knowledge structure and theme trends analysis on general practitioner research: A Co-word perspective

Bibliometric analysis and research trends of artificial intelligence in lung cancer

Background

Due to the rapid advancement of technology, artificial intelligence (AI) has become extensively used for the diagnosis and prognosis of various diseases, such as lung cancer. Research in the field of literature has demonstrated that artificial intelligence (AI) can be valuable in the timely detection of lung cancer and the formulation of an effective treatment plan. This study aims to conduct a bibliometric analysis to examine and illustrate the specific areas of focus, research frontiers, evolutionary processes, and trends in existing research on artificial intelligence in the context of lung cancer.

Methods

Publications on AI in lung cancer were selected from the SCIE and ESCI indexes on September 19, 2023. The examination of nations, academic publications, organizations, writers, citations, and terms in this domain was visually analyzed with InCites and VOSviewer.

Results

In this study, a total of 4275 publications were selected and analyzed. Artificial intelligence-related lung cancer publications have increased significantly in the last 5 years. China and the USA have contributed the most to the literature in this field (1418 publications with 13.92 citation impacts and 1117 publications with 37.34 citation impacts, respectively). The institution with the highest contribution was "Chinese Academy of Sciences," with 118 publications and 29.09 citation impacts. Among the research categories, "Radiology, Nuclear Medicine & Imaging", "Oncology", and "Engineering, Biomedical" were in first place.

Conclusion

The USA and China have always been leaders in this field and will continue to be for some time. Research in countries such as the Netherlands is increasing. However, research collaboration has to be strengthened in developing countries.

Research hotspots and trends in internal fixation of femoral neck fractures from 2010 to 2022: A 12-year bibliometric analysis

BACKGROUND

This study endeavors to scrutinize the hotspots and trends in the literature concerning the internal fixation of femoral neck fractures (INFNF) through a comprehensive bibliometric analysis. Notably, this analytical process encompasses both qualitative and quantitative components.

METHODS

The present study has utilized the Science Citation Index-Expanded from the Web of Science Core Collection to extract datasets ranging from January 1, 2010, to August 31, 2022. Quantitative analysis was carried out using sophisticated analytical tools such as the Bibliographic Item Co-Occurrence Matrix Builder, the Online Analysis Platform of Literature Metrology, and CiteSpace software. Further, the major Medical Subject Headings terms and their subheading counterparts associated with INFNF were extracted from the PubMed2XL website using the corresponding PMIDs. These Medical Subject Headings terms were employed in conducting a co-word clustering analysis. Ultimately, the Graphical CLUstering TOolkit program was utilized to execute a co-word biclustering analysis to discern the prevailing hotspots in this domain.

RESULTS

Between January 1, 2010, and August 31, 2022, a total of 463 publications were issued on INFNF. The INJURY-INTERNAL JOURNAL OF THE CARE OF THE INJURED stood out as the most extensively perused journal in this area. Notably, China emerged as the foremost contributor to publishing articles within the last 12 years, followed by the United States and Canada. McMaster University was identified as the leading institution in INFNF research, while Bhandari M emerged as the most prolific author in this field. Moreover, the study identified five notable research hotspots within the domain of INFNF.

CONCLUSIONS

This study has identified five critical areas of research in the field of INFNF. It suggests that the primary focus of future research is likely to center on advancing internal fixation methods and robot-assisted instrumentation for femoral neck fractures. As such, this study provides valuable insights into future research directions and ideas for those working in this field.

Temporal and Geographic Patterns of Documentation of Sexual Orientation and Gender Identity Keywords in Clinical Notes

OBJECTIVE

Disclosure of sexual orientation and gender identity correlates with better outcomes, yet data may not be available in structured fields in electronic health record data. To gain greater insight into the care of sexual and gender-diverse patients in the Veterans Health Administration (VHA), we examined the documentation patterns of sexual orientation and gender identity through extraction and analyses of data contained in unstructured electronic health record clinical notes.

METHODS

Salient terms were identified through authoritative vocabularies, the research team's expertise, and frequencies, and the use of consistency in VHA clinical notes. Term frequencies were extracted from VHA clinical notes recorded from 2000 to 2018. Temporal analyses assessed usage changes in normalized frequencies as compared with nonclinical use, relative growth rates, and geographic variations.

RESULTS

Over time most terms increased in use, similar to Google ngram data, especially after the repeal of the "Don't Ask Don't Tell" military policy in 2010. For most terms, the usage adoption consistency also increased by the study's end. Aggregated use of all terms increased throughout the United States.

CONCLUSION

Term usage trends may provide a view of evolving care in a temporal continuum of changing policy. These findings may be useful for policies and interventions geared toward sexual and gender-diverse individuals. Despite the lack of structured data, the documentation of sexual orientation and gender identity terms is increasing in clinical notes.

Research hotspots and theme trends in post-traumatic growth: A co-word analysis based on keywords

Objectives

To analyze and summarize the research hotspots and advancement of post-traumatic growth (PTG) over the past 15 years based on co-word analysis of keywords, and provide references for PTG-related research and clinical intervention.

Methods

All studies related to PTG were retrieved from PubMed and Web of Science (WOS) from January 2013 to July 2022. A total of 11 Medical Subject Headings (MeSH) and keywords were used to identify qualified studies. Bibliographic Item Co-occurrence Matrix Builder (BICOMB; version 2.0) was used to conduct high-frequency keywords extraction and matrix setup, Graphical Clustering Toolkit (gCLUTO; version 1.0) was employed to perform clustering analysis, and SPSS (version 25.0) was used to carry out strategic diagram analysis.

Results

A total of 2,370 publications were selected, from which 38 high-frequency keywords were extracted. The results revealed six research hotspots on PTG during the period from 2013 to 2022, including research on i) emotional reactions after negative life events, ii) PTG among cancer survivors, iii) rumination and resilience after trauma, iv) PTG among children and adolescents, v) role of social support and coping strategy in PTG, and vi) association between PTG and quality of life.

Conclusions

This co-word analysis effectively reveals an overview of PTG over the past 15 years. The six research categories deduced from this study can reflect that the research content in the field of PTG is abundant, but some research topics have not yet been mature. The findings of this study are of great value to future investigations associated with PTG.

Biometrics Data Visualization of Ginsenosides in Anticancer Investigations

Ginsenoside extracts have been shown to have anticancer effects by a growing number of studies and have thus become a hot topic in cancer research. Our study used VOSviewer and CiteSpace softwares to conduct a bibliometric approach to co-citation and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field of cancer research to investigate the current status and trends of ginsenosides research in cancer. The web of science core collection (WoSCC) contained a total of 1102 papers. China made the most contributions in this area, with the most publications (742, 67.3%), and collaborated closely with Korea and the USA. The Journal of Ginseng Research, with the most total citations (1607) and an IF of 6.06, is the leading journal in the field of ginsenoside and cancer research, publishing high quality articles. Saponin and its extracts inhibit oxidative stress, promote apoptosis, and inhibits chemotherapy resistance by ginsenosides, all of which are hot research areas in this field. In the coming years, it is expected that the combination of ginsenosides and nanoparticles, in-depth mechanisms of cancer inhibition, and targeted therapy will receive widespread attention.

A Signaling Game of Family Doctors and Residents from the Perspective of Personalized Contracted Service

The role of the family doctor contracted service system in China's medical and health system is increasing day by day. However, with the steady increase in contracting coverage, the phenomenon of "signing up but not contracting" has become common; to improve the current situation, the personalized signing service model has been strongly advocated. To promote the smooth implementation of the personalized contracted service model with family doctor competency as its core, this study used the signal game model to analyze the market equilibrium state of the signing service model. The results of this analysis reveal the following: (1) The camouflage of the number of contracts leads to distortion of the signal effect and to market failure, that is, the cost of competency camouflage is the primary factor affecting the equilibrium of contracted services. (2) The incompleteness of contracted services leads to quantity but not quality in the contracting market, that is, the payment of personalized service packages, the value-added utility of personalized services, and service gaps are the key factors that affect the decision-making behavior of the public. With this knowledge in mind, a compensation incentive mechanism that matches the competence level of the family doctor should be established, the formulation of contracted service agreements should be improved, and the participation of family doctors and residents should be encouraged, while the promotion of personalized contracted services should be enhanced and relevant supporting measures should be improved.

Cluster analysis and network visualization of research in mucormycosis: a scientometric mapping of the global publications from 2011 to 2020

Purpose

Mucormycosis has evolved as a post-COVID-19 complication globally, especially in India. The research on fungus has been very primitive, and many scientific publications have been discovered. The current COVID-19 pandemic needs further investigation into this unusual fungal infection. This review study aims to provide a pen-picture to researchers, science policymakers and scientists about different bibliometric indicators related to the research literature on mucormycosis.

Design/methodology/approach

The quantitative research was conducted using the established procedure of bibliometric investigation on data collected from Scopus from 2011 to 2020 using a validated search query. The search query consisted of keywords “Mucormycosis” or “Mucormycoses” or “Mucormycose” or “Mucorales Infection” or “Mucorales Infections” or “Black Fungus Infection” or “Black Fungus Infections” or “Zygomycosis” in the “Title-Keyword-Abstract” search option for data extraction. The analysis of data is performed using MS-Excel. Mapping was done with state-of-the-art visualization tools Biblioshiny and VOSviewer, using bibliometric indicators as units of analysis.

Findings

The analysis reveals that the first publication on this topic was reported from 1923 onwards. In total, 9,423 authors contributed 1,896 papers with 11,437 collaborated authors, documents per author are 0.201, authors per document are 4.97 and co-authors per document are 6.03. Total records were published in 779 journals in the English language from 75 countries globally. Mucormycosis literature is mostly open access, with 1,210 publications available via different open access routes. The highest number of articles (204) published in the journal “Mycoses” with 1,333 authors received 4,875 cited references, and the h-index has 24. The growth of publications is exponential, as depicted by the Price Law. The USA has recorded a maximum number of publications at both country and institutional levels compared to the other nations. There has been extensive research on mucormycosis before the outbreak as a post-COVID complication, as indicated by the highest number of publications in 2019.

Practical implications

The research hot spots have altered from “Mucormycosis,” “fungi,” “Zygomycosis” and “Drug efficacy”, “Drug Safety” to “Microbiology,” “Pathology,” “nucleotide sequence,” “surgical debridement” which indicates that potential area of research in the near future will be concerned with more extensive research in mucormycosis to develop standard treatment procedures to fight this infection. The quantity of scientific publications has also increased over time. The research and health community are called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for health authorities in different nations to leverage vaccination in its transformational role toward successfully attaining nil cases of COVID-19.

Originality/value

The analysis of collaboration, findings, the research networks and visualization makes this study novel and separates from traditional metrics analysis. To the best of the authors’ knowledge, this work is original, and no similar studies have been found with the objectives included here.

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

Background

With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.

Objective

This study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.

Methods

The articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results

A total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.

Conclusions

Artificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

Bibliometric Analysis of Global Research Trends on Ultrasound Microbubble: A Quickly Developing Field

Background: Microbubbles are widely used as highly effective contrast agents to improve the diagnostic capability of ultrasound imaging. Mounting evidence suggests that ultrasound coupled with microbubbles has promising therapeutic applications in cancer, cardiovascular, and neurological disorders by acting as gene or drug carriers. The aim of this study was to identify the scientific output and activity related to ultrasound microbubble through bibliometric approaches. Methods: The literature related to ultrasound microbubble published between 1998 and 2019 was identified and selected from the Science Citation Index Expanded of Web of Science Core Collection on February 21, 2021. The Scopus database was also searched to validate the results and provided as supplementary material. Quantitative variables including number of publications and citations, H-index, and journal citation reports were analyzed by using Microsoft Excel 2019 and GraphPad Prism 8.0 software. VOS viewer and CiteSpace V were used to perform coauthorship, citation, co-citation, and co-occurrence analysis for countries/regions, institutions, authors, and keywords. Results: A total of 6088 publications from the WoSCC were included. The United States has made the largest contribution in this field, with the majority of publications (2090, 34.3%), citations (90,741, 46.6%), the highest H-index (138), and close collaborations with China and Canada. The most contributive institution was the University of Toronto. Professors De Jong N and Dayton P A have made great achievements in this field. However, the research cooperation between institutions and authors was relatively weak. All the studies could be divided into four clusters: "ultrasound diagnosis study," "microbubbles' characteristics study," "gene therapy study," and "drug delivery study." The average appearing years (AAY) of keywords in the cluster "drug delivery study" was more recent than other clusters. For promising hot spots, "doxorubicin" showed a relatively latest AAY of 2015.49, followed by "nanoparticles" and "breast cancer." Conclusion: There has been an increasing amount of scientific output on ultrasound microbubble according to the global trends, and the United States is staying ahead in this field. Collaboration between research teams still needs to be strengthened. The focus gradually shifts from "ultrasound diagnosis study" to "drug delivery study." It is recommended to pay attention to the latest hot spots, such as "doxorubicin," "nanoparticles," and "breast cancer."

Mapping theme trends and recognizing hot spots in postmenopausal osteoporosis research: a bibliometric analysis

Background

This study aimed to draw a series of scientific maps to quantitatively and qualitatively evaluate hot spots and trends in postmenopausal osteoporosis research using bibliometric analysis.

Methods

Scientific papers published on postmenopausal osteoporosis were extracted from the Web of Science Core Collection and PubMed database. Extracted information was analyzed quantitatively with bibliometric analysis by CiteSpace, the Online Analysis Platform of Literature Metrology and Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). To explore the hot spots in this field, co-word biclustering analysis was conducted by gCLUTO based on the major MeSH terms/MeSH subheading terms-source literatures matrix.

Results

We identified that a total of 5,247 publications related to postmenopausal osteoporosis were published between 2013 and 2017. The overall trend decreased from 1,071 literatures in 2013 to 1,048 literatures in 2017. Osteoporosis International is the leading journal in the field of postmenopausal osteoporosis research, both in terms of impact factor score (3.819) and H-index value (157). The United States has retained a top position and has exerted a pivotal influence in this field. The University of California, San Francisco was identified as a leading institution for research collaboration, and Professors Reginster and Kanis have made great achievements in this area. Eight research hot spots were identified.

Conclusions

Our study found that in the past few years, the etiology and drug treatment of postmenopausal osteoporosis have been research hot spots. They provide a basis for the study of the pathogenesis of osteoporosis and guidelines for the drug treatment of osteoporosis.

Identification of research trends concerning application of stent implantation in the treatment of pancreatic diseases by quantitative and biclustering analysis: a bibliometric analysis

OBJECTIVES

In recent years, with the development of biological materials, the types and clinical applications of stents have been increasing in pancreatic diseases. However, relevant problems are also constantly emerging. Our purpose was to summarize current hotspots and explore potential topics in the fields of the application of stent implantation in the treatment of pancreatic diseases for future scientific research.

METHODS

Publications on the application of stents in pancreatic diseases were retrieved from PubMed without language limits. High-frequency Medical Subject Headings (MeSH) terms were identified through Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). Biclustering analysis results were visualized utilizing the gCLUTO software. Finally, we plotted a strategic diagram.

RESULTS

A total of 4,087 relevant publications were obtained from PubMed until May 15th, 2018. Eighty-three high-frequency MeSH terms were identified. Biclustering analysis revealed that these high-frequency MeSH terms were classified into eight clusters. After calculating the density and concentricity of each cluster, strategy diagram was presented. The cluster 5 "complications such as pancreatitis associated with stent implantation" was located at the fourth quadrant with high centricity and low density.

CONCLUSIONS

In our study, we found eight topics concerning the application of stent implantation in the treatment of pancreatic diseases. How to reduce the incidence of postoperative complications and improve the prognosis of patients with pancreatic diseases by stent implantation could become potential hotspots in the future research.

Mapping theme trends and knowledge structures for human neural stem cells: a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells, which are capable of multi-potential differentiation and self-renewal, have recently been shown to have clinical potential for repairing central nervous system tissue damage. However, the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically. In this study, we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word. Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies. Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder. We identified 78 high-frequency Medical Subject Heading (MeSH) terms. A visual matrix was built with the repeated bisection method in gCLUTO software. A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software. The analyses demonstrated that in the 6-year period, hot topics were clustered into five categories. As suggested by the constructed strategic diagram, studies related to cytology and physiology were well-developed, whereas those related to neural stem cell applications, tissue engineering, metabolism and cell signaling, and neural stem cell pathology and virology remained immature. Neural stem cell therapy for stroke and Parkinson's disease, the genetics of microRNAs and brain neoplasms, as well as neuroprotective agents, Zika virus, Notch receptor, neural crest and embryonic stem cells were identified as emerging hot spots. These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

A bibliometric analysis of long non-coding RNA and chemotherapeutic resistance research

The global outputs of annual publication in long non-coding RNAs (lncRNAs) and chemotherapeutic resistance research exponentially increased from 2 in 2008 to 176 in 2017. Using Java application CiteSpace V and VOSviewer, this study assessed the publication model of lncRNAs and chemoresistance by bibliometric analysis. Totally, 2883 authors contributed 528 publications of lncRNAs and chemoresistance in 215 academic journals in the recent decade (2008-2018). Oncotarget in the 215 academic journals published the highest number of publications (60). China had the highest number of publication outputs (358). The leading institute was Nanjing Medical University. Wang Y was the most influential author (13 counts). Gupta RA had the most cited documents (87 counts). “Gene expression” and “poor prognosis” were identified as the hotspots. “Cancer stem cell”, “HOTAIR” and “UCA1” were the frontiers of the fields in recent years. The increase of publications on lncRNAs and chemotherapeutic resistance will continue in the next years. HOTAIR and UCA1 with multiple roles in drug resistance may offer big opportunities for targeted chemoresistance in cancer therapy. These results may help us discover and explain the possible underlying laws of the subject.

Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective

Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations. The MEDLINE database was summarized as a complex system, consisting of nodes and edges, where the nodes refer to knowledge concepts and the edges symbolize corresponding relations. We performed an extensive statistical evaluation based on more than 25 million citations in the MEDLINE database, from 1966 until 2014. We based our analysis on node and community level in order to track temporal evolution in the network. The degree distribution of the network follows a stretched exponential distribution which prevents the creation of large hubs. Results showed that the appearance of new MeSH terms does not also imply new connections. The majority of new connections among nodes results from old MeSH descriptors. We suggest a wiring mechanism based on the theory of structural holes, according to which a novel scientific discovery is established when a connection is built among two or more previously disconnected parts of scientific knowledge. Overall, we extracted 142 different evolving communities. It is evident that new communities are constantly born, live for some time, and then die. We also provide a Web-based application that helps characterize and understand the content of extracted communities. This study clearly shows that the evolution of MEDLINE knowledge correlates with the network's structural and temporal characteristics.

Theme trends and knowledge structure on choroidal neovascularization: a quantitative and co-word analysis

Background

The distribution pattern and knowledge structure of choroidal neovascularization (CNV) was surveyed based on literatures in PubMed.

Methods

Published scientific papers about CNV were retrieved from Jan 1st, 2012 to May 31st, 2017. Extracted MeSH terms were analyzed quantitatively by using Bibliographic Item Co-Occurrence Matrix Builder (BICOMB) and high-frequency MeSH terms were identified. Hierarchical cluster analysis was conducted by SPSS 19.0 according to the MeSH term-source article matrix. High-frequency MeSH terms co-occurrence matrix was constructed to support strategic diagram and social network analysis (SNA).

Results

According to the searching strategy, all together 2366 papers were included, and the number of annual papers changed slightly from Jan 1st, 2012 to May 31st, 2017. Among all the extracted MeSH terms, 44 high-frequency MeSH terms were identified and hotspots were clustered into 6 categories. In the strategic diagram, clinical drug therapy, pathology and diagnosis related researches of CNV were well developed. In contrast, the metabolism, etiology, complications, prevention and control of CNV in animal models, and genetics related researches of CNV were relatively immature, which offers potential research space for future study. As for the SNA result, the position status of each component was described by the centrality values.

Conclusions

The studies on CNV are relatively divergent and the 6 research categories concluded from this study could reflect the publication trends on CNV to some extent. By providing a quantitative bibliometric research across a 5-year span, it could help to depict an overall command of the latest topics and provide some hints for researchers when launching new projects.

Bibliometric analysis of “Internet-plus”

Purpose

The purpose of this paper is to gain an understanding of the current research on Internet-plus.

Design/methodology/approach

The authors collected scholarly publications from the scientific databases Web of Science, Core Collection, Inspec and Compendex (Ei Village 2) and performed statistical analysis of the retrieved data from five perspectives.

Findings

The research on Internet-plus has obtained increasing attention in China. The top three research fields were social science, education and management.

Originality/value

The study will help researchers understand the current trends on Internet-plus.

Trends in Fetal Medicine: A 10-Year Bibliometric Analysis of Prenatal Diagnosis

The objective is to automatically identify trends in Fetal Medicine over the past 10 years through a bibliometric analysis of articles published in Prenatal Diagnosis, using text mining techniques. We processed 2,423 full-text articles published in Prenatal Diagnosis between 2006 and 2015. We extracted salient terms, calculated their frequencies over time, and established evolution profiles for terms, from which we derived falling, stable, and rising trends. We identified 618 terms with a falling trend, 2,142 stable terms, and 839 terms with a rising trend. Terms with increasing frequencies include those related to statistics and medical study design. The most recent of these terms reflect the new opportunities of next-generation sequencing. Many terms related to cytogenetics exhibit a falling trend. A bibliometric analysis based on text mining effectively supports identification of trends over time. This scalable approach is complementary to analyses based on metadata or expert opinion.