Artículo

Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses

Resumen

Aims The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal’s website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.
Fijačko, Nino (56801806500); Creber, Ruth Masterson (58689666300); Abella, Benjamin S. (6603294085); Kocbek, Primož (57201637234); Metličar, Špela (58310112000); Greif, Robert (25651608800); Štiglic, Gregor (6506110486)
Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses
2024
10.1016/j.resplu.2024.100584
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186090938&doi=10.1016%2fj.resplu.2024.100584&partnerID=40&md5=719069cae5e6b27f588a1ece190df63b
University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; ERC Research Net, Niels, Belgium; Maribor University Medical Centre, Maribor, Slovenia; Columbia University School of Nursing, New York, NY, United States; Center for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States; University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia; Medical Dispatch Centre Maribor, University Clinical Centre Ljubljana, Ljubljana, Slovenia; University of Bern, Bern, Switzerland; School of Medicine, Sigmund Freud University Vienna, Vienna, Austria; University of Maribor, Faculty of Electrical Engineering and Computer Science, Maribor, Slovenia; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
All Open Access; Gold Open Access
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