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Authorship transparency and equity in otolaryngology and maxillofacial surgery: current practices and the potential impact of large language models

The determination of authorship order in scientific publications is critical for researcher recognition and career progression. According to a recent survey's meta-analysis, the prevalence of Honorary Authorship reaches 20% of authors. This study evaluates the adoption and proficency of authors...

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Bibliographic Details
Published in:European archives of oto-rhino-laryngology 2024-12
Main Authors: Frosolini, Andrea, Benedetti, Simone, Gennaro, Paolo, Gabriele, Guido
Format: Article
Language:English
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Summary:The determination of authorship order in scientific publications is critical for researcher recognition and career progression. According to a recent survey's meta-analysis, the prevalence of Honorary Authorship reaches 20% of authors. This study evaluates the adoption and proficency of authorship guidelines in maxillofacial surgery and otolaryngology. On March 16, 2024, a search of Scopus-indexed journals identified relevant journals in from which recent articles were sampled using a stratified random method. The author contribution alignment with actual authorship was evaluated through CalculAuthor tool and a detailed process involving large language models (LLM) and manual checks, employing statistical tests to evaluate the adoption of author contribution guidelines and agreement scores across different fields and journal quartiles. A review of 21 Scopus-indexed journals revealed that 8 adhered to the CRediT system, 7 to other systems, and 6 had no guidelines. From 147 randomly selected manuscripts, 57 reported contributions (38.8%). The mean agreement score was 65.3% (SD = 26.8), without significant differences in journal quartiles or disciplines. This study highlights the moderate to low adoption of standardized contribution systems in maxillofacial and otolaryngology journals, with a fair level of agreement between reported contributions and actual authorship. Future research should focus on larger, more diverse samples and develop prospective implementations to ensure accurate and equitable authorship credit in the context of the Artificial Intelligence's scientific writing era.
ISSN:0937-4477
1434-4726
1434-4726
DOI:10.1007/s00405-024-09151-5