Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures

This AutoML model, validated through explainability techniques, confirms the core predictive value of age, operative duration, and coagulation-inflammation networks for adolescent tibial fracture risk management. Though requiring prospective validation, the three-tier warning system establishes a stepped framework for individualized intervention. Future studies should advance multicenter collaborations integrating dynamic monitoring indicators to optimize clinical applicability.