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. 2024 Feb 22:18:100584.
doi: 10.1016/j.resplu.2024.100584. eCollection 2024 Jun.

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

Nino Fijačko  1   2   3 , Ruth Masterson Creber  4 , Benjamin S Abella  5 , Primož Kocbek  1   6 , Špela Metličar  1   7 , Robert Greif  2   8   9 , Gregor Štiglic  1   10   11
Affiliations

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

Nino Fijačko et al. Resusc Plus. .

Abstract

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.

Keywords: Bibliometrics analysis; Congress; Emergency medicine; European Resuscitation Council; Generative artificial intelligence.

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Conflict of interest statement

Nino Fijačko is a member of the ERC BLS Science and Education Committee. Robert Greif is ERC Director of Guidelines and ILCOR, and ILCOR Task Force chair for Education Implementation and Team. Other authors declare that they have no conflict of interest. Benjamin S. Abella reported serving on the American Heart Association Resuscitation Science Symposium committee.

Figures

Fig. 1
Fig. 1
Proportion of categorized abstract titles using ChatGPT-4 API with respect to ERC guidelines topics grouped into three districts groups.
Fig. 2
Fig. 2
Visualisation of co-occurrence of ERC guidelines topics.
See this image and copyright information in PMC

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