Risk assessment for intraabdominal injury following blunt trauma in children: Derivation and validation of a machine learning model

This study applies machine learning to the PECARN blunt abdominal trauma dataset to create predictive models for intra-abdominal injury requiring intervention in children. The models aim to provide individualized risk assessment beyond existing low-risk criteria, potentially reducing unnecessary CT scans while improving clinical decision-making for pediatric trauma patients.