Background: Laparoscopic cholecystectomy is the preferred treatment for symptomatic cholelithiasis and acute cholecystitis, with increasing applications even in severe cases. However, the possibility of postoperative endoscopic retrograde cholangiopancreatography (ERCP) to manage choledocholithiasis or biliary injuries poses significant clinical challenges. This study aimed to develop a predictive model for ERCP incidence following emergency laparoscopic cholecystectomy using advanced machine learning techniques.
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