Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship over 10 Years: A Deep Learning Linguistic Analysis.

Vikram VasanChristopher P ChengCaleb J FanDavid K LernerKaren PascualAlfred Marc IloretaSeilesh C BabuMaura K Cosetti

Published in: Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology (2024)

Linguistic content overall favored male applicants because they were more frequently described as trustworthy and leaders. However, the temporal analysis of linguistic differences between male and female applicants found an encouraging trend suggesting a reduction of gender bias in recent years, mirroring an increased composition of women in neurotology over time.

Keyphrases

deep learning
polycystic ovary syndrome
clinical practice
machine learning
convolutional neural network
pregnant women
metabolic syndrome