Objectives: We aimed to perform a bibliometric analysis of original research articles on Behçet's syndrome (BS) published over the last 20 years prior to the COVID-19 pandemic, and to systematically describe their characteristics and citation records.
Methods: The PubMed database was searched for any article published on BS between 2000 and 2019. We identified all original research articles and categorised them by country of origin and type of research, i.e., clinical, translational and basic. Each article's impact was assessed using the individual citation numbers from Google Scholar search engine; we also calculated the median annual citation rates (ACRs), both per country and research type.
Results: Of a total of 2,381 retrieved original articles from 51 countries, the majority reported on clinical (52.6%), followed by translational (46.0%) and basic research (1.4%). Turkey had the highest number of publications (39% of articles) followed by four countries (Korea, China, Japan, Italy) where BS is also relatively prevalent. However, regarding median ACRs, France was first, followed by the United Kingdom, Germany and Collaboration. Although the number of articles has almost doubled between 2010-2019 versus 2000-2009, median ACRs across either clinical or translational research had a downwards trend.
Conclusions: Researchers from countries where BS is prevalent are more productive, albeit their work is of lower impact compared to countries with generally higher research budgets. A considerable increase of original research articles on BS is observed over time but further funding may be warranted for a parallel increase in the respective scientific impact.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.55563/clinexprheumatol/rq72g6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Climate change impact on green spaces planning in an urban area using a hybrid approach.
Environ Sci Pollut Res Int
January 2025
Department of Geomatics Engineering, Hacettepe University, 06800, Beytepe, Ankara, Türkiye.
This study presents a hybrid methodology for planning green spaces to enhance urban sustainability and livability, evaluating the impacts of climate change on cities. Cities, once accommodating a small population, have become major centers of migration and development since the eighteenth century. Rapid urban growth intensifies infrastructure, environmental, and social challenges.
View Article and Find Full Text PDFCorrelations between postmortem quantitative MRI parameters and demyelination, axonal loss and gliosis in multiple sclerosis: A systematic review and meta-analysis.
Brain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFEmoAtlas: An emotional network analyzer of texts that merges psychological lexicons, artificial intelligence, and network science.
Behav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
View Article and Find Full Text PDFDeep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.
Med Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!