Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips

Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips

Background: Controversy remains about whether to pin the contralateral side in cases of unilateral slipped capital femoral epiphysis (SCFE). Machine learning (ML) algorithms can be leveraged to identify complex, nonlinear patterns in data and allow for more accurate predictions on which patients may...

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Bibliographic Details
Main Authors: David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Journal of the Pediatric Orthopaedic Society of North America
Subjects:
Machine learning
Slipped capital femoral epiphysis (SCFE)
Contralateral
Prophylactic
Prediction
Online Access:http://www.sciencedirect.com/science/article/pii/S2768276525000525
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