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Published on: March 8, 2012
A Holistic Bibliometric Exploration of the Evolution of Epidermodysplasia Verruciformis (EV) Research
Farshid Danesh1, Fatemeh Alinezhad Chamazcoti2, Forough Rahimi1
1Department of Information Management, Islamic World Science and Technology Monitoring and Citation Institute (ISC), Shiraz, Iran.
Background:
Epidermodysplasia Verruciformis (EV) is a rare autosomal recessive genodermatosis caused by chronic HPV infection. It leads to widespread cutaneous lesions and an increased risk of non-melanoma skin cancer. The main objective of this article is to conduct a holistic bibliometrics analysis of scientific publications, uncovering trends and citation networks related to EV (1931-2025).
Materials And Methods:
Data were extracted from the Web of Science Core Collection (1931-2025), and 1489 documents were retrieved. Bibliometric methods and related indicators, including country collaboration map, word co-occurrence, co-citation networks, topics, citations, and publications trends, were analyzed and visualized using the Bibliometrix package in R.
Results:
The document collection comprises 99 single-authored publications, with a Document Average Age (DAA) of 20.2 years (i.e., the average time elapsed between publication and the year of analysis, 2025), 5.36 authors per document, 32.15 citations per document, and a 2.09% annual growth rate. The five most frequent thematic trends are EV, deoxyri bonucleic acid (DNA), Squamous Cell Carcinoma, Renal Transplant Recipients, and Infection, with frequencies of 423, 167, 127, 121, and 100. The United States, Germany, and Great Britain have the highest numbers of citations, with 13,998, 6,476, and 4,776. "G. Orth," "S. Jablonska," and "M. Favre" received the highest number of citations in EV. The most influential journals are "Journal of Virology," "Journal of Investigative Dermatology," and "Virology".
Conclusion:
Epidemiological studies on EV prevalence and high-risk human papillomavirus (HPV) types are crucial for policymakers and health planners. Interdisciplinary research, using artificial intelligence, image processing, and machine learning, can enhance research and collaboration.
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