Producción Científica

Background and Objectives: Citation analysis is a fundamental method in bibliometrics for quantifying the impact and contribution of articles on a specific biomedical field. The purpose of our study was to identify and analyze the top 100 cited articles in the field of EUS. Methods: All published articles in the field of EUS were searched by using “endoscopic ultrasound” and its synonyms as the search terms without time limit. The Institute for Scientific Information Web of Science Core database was searched to determine the citations. The top 100 cited articles were identified and further evaluated for characteristics including publication year, authors, journals, impact factor, countries, institutions, article type, topic term, and evidence grade, among others. Results: A total of 430 articles were cited more than 100 times. The 100 most-cited articles were published between 1988 and 2018, and the medium citation was 240.5 (104.25). The top 100 cited articles mainly focused on diagnostic performance (80%) and interventional therapy (20%). The numbers of articles studying the diagnostic accuracy of FNA (n = 29) and tumor diagnosis (n = 29) were the highest among research articles on FNA and EUS of diagnostic categories, and EUS transluminal drainage (n = 14) was the most frequently used EUS technique for therapy. The focus of the majority of the articles was on diseases of pancreas (n = 55), and among the 55 articles related to pancreatic diseases, pancreatic cancer (n = 17) and solid pancreatic masses (n = 13) were the most researched topics. In addition, we found that the proportions of diagnostic and treatment-related articles at different time periods have statistical significance (P < 0.05). Conclusions: Our analysis provides an insight into the top 100 articles in the field of EUS, revealing EUS-guided FNA, tumor staging, and transluminal drainage as the major advances in the past 35 years. Pancreatic diseases were the most researched, especially pancreatic cancer or solid pancreatic masses. Our research has found that the number of articles on the application of EUS treatment has significantly increased.

An infection that can occur in any part of the urinary system is known as a urinary tract infection (UTI). The urinary system comprises the bladder, urethra, ureters, and kidneys. The majority of infections affect the lower urinary system’s bladder and urethra. This study presents a scientometric analysis of authorship patterns in Urinary Tract Infection (UTI) and Diabetes. The study focuses on Lotka’s law to understand the productivity and impact of authors in the field. For this study, 1149 documents were retrieved from the Web of Science database from 2009 to 2023. The USA leads in publications on UTIs and diabetes among all countries. Among all authors, Kuku K has been the most productive author. K-S test reveals that the current data set does not support Lotka’s law’s applicability to research on urinary tract infections and diabetes. The findings of the study suggest that there is a need for more research to be done to improve the understanding of the relationship between UTI and Diabetes.

Health communication is one of the crucial fields of contemporary research, especially in the post-Covid-19 epidemic era. In the last decade (2013-2022), however, no bibliometric performance analysis of the health communication (HC) overall has been conducted.To investigate research performance in HC, including performance of different countries/regions, institutions/groups, authors, journals, research areas, as well as status of collaboration and funding support. On January 25, 2023, a search for topic terms and article sources was carried out using Web of Science Core Collection. Then, duplicate data were removed, and a manual screening process was implemented. Bibliometric indicators were selected. Finally, data collection, analysis, and graph were performed using Excel 2019 and Origin 8.5.The quantity of publications in HC has steadily risen from 2013, and experienced rapid growth after 2019, whereas these publications’ overall impact has been on a decline. The articles are predominantly situated within the fields of social sciences, medicine, environmental science, and science and technology, for these areas benefit from the highest level of financial support. USA continues to hold an absolute leadership position in research productivity, reflected in the preponderance of prolific institutions and authors affiliated with USA. Additionally, among the top productive18 journals, half are OA, and importantly, their h-indices tend to be on par with non-OA journals. At last, collaboration between authors and institutions is widespread, however, the degree of international collaboration is relatively low. For HC research, diverse nations should strive to overcome cultural and political barriers, fostering stronger research collaborations. The diminishing influence of HC articles over the years may not be a reflection of a decline in scholarly quality; instead, it might be the integration of knowledge with substantial heterogeneity.

Motivation: Citations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications. Results: We manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model—which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification—yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy.