Annals of surgery
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To evaluate machine learning models' performance in predicting acute pancreatitis severity using early-stage variables while excluding laboratory and imaging tests. ⋯ The ML models showed good predictive capacity for SAP, ICU admission, and mortality using early-stage data without laboratory or imaging tests. This approach could revolutionise AP patients' initial triage and management, providing a personalised prediction method based on early clinical data.
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To clarify the impact of the preoperative time intervals on short-term postoperative and pathologic outcomes in patients with esophageal cancer who underwent neoadjuvant chemoradiotherapy (nCRT) followed by esophagectomy. ⋯ In patients with esophageal cancer undergoing nCRT and esophagectomy, prolonged preoperative time intervals may lead to higher morbidity and disease progression, and the causal relationship requires further confirmation.
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Review Meta Analysis
Revolutionizing Organ Transplantation with Robotic Surgery.
The aim of this study was to evaluate the impact of robotic techniques on organ transplantation outcomes. ⋯ This comprehensive overview including a systematic review, original data, and perceptions derived from the international survey demonstrate the superiority of robotic transplant surgery (RTS) across a range of organ transplantations, for both donors and recipients. The future of RTS depends on the efforts of the surgical community in addressing challenges such as economic implications, the need for specialized surgical training for numerous surgeons, as well as wide access to robotic systems worldwide.
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Multicenter Study
Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.
To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC). ⋯ Our AI model, based on easily accessible clinical and pathologic variables, moderately predicts LNM in T2 CRC. However, the risk of false negative needs to be considered. The training of the model including more patients across western and eastern centers - differentiating between colon and rectal cancers - may improve its performance and accuracy.