TL;DR
SHAP analysis revealed that stone burden, preoperative serum creatinine, preoperative white blood cell count, stone complexity, and hydronephrosis were the most critical predictors of SFS.
This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies and demonstrates an interpretable machine learning model to effectively predict POF following fURL.
Background In patients undergoing retrograde ureteroscopic lithotripsy, the failure rate of ureteral access sheath insertion is approximately 10%. Temporary double-J stenting with delayed secondary intervention is usually required when insertion fails. However, such cases cannot be reliably identified based on routine...
BACKGROUND Accurate preoperative prediction of surgical difficulty in single-incision laparoscopic cholecystectomy (SILC) remains challenging, as models for multiport surgery are not directly applicable. METHODS This retrospective single-center study included 520 patients, who were randomly divided into a training co...
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