AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Overall, these findings suggest that a machine learning–based approach may provide complementary individualized risk stratification for postoperative stone-free status after RIRS when used alongside established clinical judgment and existing scoring systems.
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.
Stone location, particularly proximal ureteral location, showed a risk-increasing pattern in univariable analysis, SHAP interpretation, and meta-analysis, and these results may support individualized preoperative assessment before attempted placement of a planned 11/13F UAS.
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...
A nomogram based on routinely available clinical, in fl ammatory, and procedural variables showed acceptable internal performance and may assist in the early identi fi cation and targeted management of patients at high risk.
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