Objective: The objective was to assess Potsic, EAONO/JOS, STAMCO, and ChOLE cholesteatoma staging systems in a large homogenous pediatric cohort with long-term follow-up and propose an evidence-based improved version.
Study Design: Cohort study.
Setting: Retrospective study in a tertiary referral center.
Purpose: There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting.
View Article and Find Full Text PDFFront Med (Lausanne)
June 2023
Otolaryngol Head Neck Surg
January 2024
Objective: To evaluate the accuracy, sensitivity, and specificity of nonecho planar (non-EPI) diffusion-weighted (DW) magnetic resonance imaging (MRI) to detect residual cholesteatoma in children.
Study Design: Retrospective study.
Setting: Tertiary comprehensive hospital.