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Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures
0
Zitationen
5
Autoren
2025
Jahr
Abstract
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.
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