Limon, Gülsüm

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Profile URL
https://hdl.handle.net/20.500.14365/6960
Name Variants
Limon, Gulsum
Job Title
Email Address
gulsum.limon@ieu.edu.tr
Main Affiliation
09.02. Internal Sciences
Status
Current Staff
Website
ORCID ID
0000-0003-3574-7633
Scopus Author ID
35746424900
Turkish CoHE Profile ID
F80F69B0EE2A28C6
Google Scholar ID
A5vED50AAAAJ
WoS Researcher ID
DDC-1344-2022

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8

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Medicine1
The American Journal of Emergency Medicine1
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  • Thumbnail Image
    Article
    A Bibliometric Analysis of Clinical Studies on Artificial Intelligence in Emergency Medicine
    (Lippincott Williams & Wilkins, 2025) Limon, Ö.; Bayram, B.; Çetin, M.; Limon, G.; Dirican, N.
    Background: Interest in artificial intelligence (AI) and machine learning (ML) has grown rapidly in recent years due to the success of modern algorithms across various domains. Emergency departments are fast-paced and resource-constrained environments where timely decision-making is critical. These characteristics make them ideal settings for the integration of AI technologies, which have shown potential to enhance diagnostic accuracy and optimize patient outcomes. This study aims to identify and characterize the scientific literature on AI and ML applications in emergency departments over the past decade. Methods: A comprehensive search was conducted in the Web of Science database on June 20, 2024. Articles published between 2015 and 2024 were considered. The search was performed using the keywords "artificial intelligence"or "machine learning"in all fields, limited to the "emergency medicine"category. The analysis of the articles included descriptive data on primary publication characteristics, such as the number of authors, citations, country of origin of the coauthors, and journal names. Bibliometric indicators were analyzed to identify publication trends and research themes, cluster analyses of keywords, and thematic maps. Results: A total of 321 articles were analyzed. The average number of citations per article was 10.04, and the annual growth rate was 37.87%. Most publications originated from the United States. Resuscitation, American Journal of Emergency Medicine, Injury-International Journal of the Care of the Injured, and Resuscitation Plus published 107 articles. In 2024, the trending topic of the articles was "health,"while "care"was the most popular in the last 10 years. The top 5 niche themes were "medical,""digital transformation,""education,""database,"and "emergency care systems."Conclusion: This bibliometric analysis highlights the growing role of AI in emergency medicine. The findings provide insight into current research directions and may help inform future investigations in this evolving field. © 2025 the Author(s).
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    Article
    An Analysis of Sample Size Calculations in Randomized Control Trials in Emergency Medicine
    (W B Saunders Co-Elsevier Inc, 2025) Limon, Onder; Dogan, Nurettin Ozgur; Limon, Gulsum; Aksay, Ersin
    Introduction: Sample size calculation enhances the quality of randomized clinical trials (RCTs) and, according to the CONSORT statement, should be reported and justified in published articles. This study aimed to evaluate the current quality of sample size calculation reporting in RCTs published in emergency medicine journals. Methods: The Web of Science (WoS) database was used for article retrieval. Journals indexed in WoS, published in English, categorized under "emergency medicine," and ranked in Q1 were included in the search. The sample size calculation method, power value, alpha value, effect size, and consideration of missing data were evaluated. Results: A total of 252 RCTs from 12 emergency medicine journals were included in the study. Only 30% of the articles explicitly stated compliance with CONSORT guidelines. Sample size calculations were reported in 84% of the articles. Alpha values were omitted in 12 % and beta (power) values in 8% of the articles. Effect sizes were not reported in 90 % of the studies. Notably, 11 % of the articles claiming CONSORT compliance did not include a sample size analysis. In the logistic regression analysis, none of the variables showed a statistically significant association with the presence of sample size analysis. Conclusion: Although emergency medicine journals show relatively better adherence to sample size calculation reporting compared to some other disciplines, their overall performance remains suboptimal. The findings highlight ongoing and significant deficiencies in the quality of RCT reporting, indicating that even leading journals in the field fall short of fully meeting recommended standards. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.