05.05.2024 | Review Article
Sociodemographic reporting in videomics research: a review of practices in otolaryngology - head and neck surgery
verfasst von:
Yeo Eun Kim, Aisha Serpedin, Preethi Periyakoil, Daniel German, Anaïs Rameau
Erschienen in:
European Archives of Oto-Rhino-Laryngology
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Ausgabe 11/2024
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Abstract
Objective
To assess reporting practices of sociodemographic data in Upper Aerodigestive Tract (UAT) videomics research in Otolaryngology-Head and Neck Surgery (OHNS).
Study design
Narrative review.
Methods
Four online research databases were searched for peer-reviewed articles on videomics and UAT endoscopy in OHNS, published since January 1, 2017. Title and abstract search, followed by a full-text screening was performed. Dataset audit criteria were determined by the MINIMAR reporting standards for patient demographic characteristics, in addition to gender and author affiliations.
Results
Of the 57 studies that were included, 37% reported any sociodemographic information on their dataset. Among these studies, all reported age, most reported sex (86%), two (10%) reported race, and one (5%) reported ethnicity and socioeconomic status. No studies reported gender. Most studies (84%) included at least one female author, and more than half of the studies (53%) had female first/senior authors, with no significant differences in the rate of sociodemographic reporting in studies with and without female authors (any female author: p = 0.2664; first/senior female author: p > 0.9999). Most studies based in the US reported at least one sociodemographic variable (79%), compared to those in Europe (24%) and in Asia (20%) (p = 0.0012). The rates of sociodemographic reporting in journals of different categories were as follows: clinical OHNS: 44%, clinical non-OHNS: 40%, technical: 42%, interdisciplinary: 10%.
Conclusions
There is prevalent underreporting of sociodemographic information in OHNS videomics research utilizing UAT endoscopy. Routine reporting of sociodemographic information should be implemented for AI-based research to help minimize algorithmic biases that have been previously demonstrated.
Level of evidence: 4.