| Title: | Using generative artificial intelligence in bibliometric analysis : 10 years of research trends from the European Resuscitation congresses |
|---|
| Authors: | ID Fijačko, Nino (Author)
ID Masterson Creber, Ruth (Author)
ID Abella, Benjamin S. (Author)
ID Kocbek, Primož (Author)
ID Metličar, Špela (Author)
ID Greif, Robert (Author)
ID Štiglic, Gregor (Author) |
|---|
| Files: |  3._Elsevier_clanek.pdf (964,04 KB)
MD5: 7E5167370BB5F757856F299408F825AA
 https://www.sciencedirect.com/science/article/pii/S2666520424000353?via%3Dihub
|
|---|
| Language: | English |
|---|
| Work type: | Scientific work |
|---|
| Typology: | 1.03 - Other scientific articles |
|---|
| Organization: | FZV - Faculty of Health Sciences
FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| 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: | generative artificial intelligence, bibliometric analysis, congress, emergency medicine, European Resuscitation Council |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 03.02.2024 |
|---|
| Article acceptance date: | 08.02.2024 |
|---|
| Publication date: | 23.02.2024 |
|---|
| Publisher: | ELSEVIER |
|---|
| Year of publishing: | 2024 |
|---|
| Number of pages: | str. 1-5 |
|---|
| Numbering: | Letn. 18, članek št. 100584 |
|---|
| PID: | 20.500.12556/DKUM-91189  |
|---|
| UDC: | 004.8:616-083.98 |
|---|
| ISSN on article: | 2666-5204 |
|---|
| COBISS.SI-ID: | 187063811 |
|---|
| DOI: | 10.1016/j.resplu.2024.100584 |
|---|
| Copyright: | © 2024 The Author(s). Published by Elsevier B.V. |
|---|
| Publication date in DKUM: | 27.11.2024 |
|---|
| Views: | 281 |
|---|
| Downloads: | 15 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
