Producción Científica

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.