A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation

The refined model incorporated four readily accessible variables, wherein the CCD, HDL levels, and blood loss were associated with an elevated risk of nonunion. Conversely, the application of nerve blocks, including postoperative block, was correlated with a reduced risk. Our results suggest that ML, particularly the CatBoost model, can be integrated into clinical workflows to aid surgeons in optimizing intraoperative techniques and postoperative management to reduce nonunion rates.