Publications by authors named "M Malago"
Background And Aims: Given the increasing demand of patients requiring liver transplant who are 70 years or older and have may have health conditions, this study aimed to assess the outcomes of Living Donor Liver Transplant (LDLT) recipients, in this age group.
Methods: We conducted an analysis using a prospective registry that included all LDLT recipients from January 2011 to May 2023. Patients into two age groups; 18-69 years and 70 years or older and compared their short- term and long-term outcomes.
Background: Failure to rescue (FTR) is defined as the inability to prevent death after the development of a complication. FTR is a parameter in evaluating multidisciplinary postoperative complication management. The aim of this study was to evaluate FTR rates after major liver resection for perihilar cholangiocarcinoma (pCCA) and analyze factors associated with FTR.
View Article and Find Full Text PDFObjective: This study aimed to assess short-term biliary outcomes in adult living donor liver transplants using right grafts, comparing robotic surgery with real-time indocyanine green fluorescence cholangiography for optimal hilar plate transection, against the conventional open approach.
Background: Determining the optimal transection plane through the hilar plate is crucial in donor hepatectomies, impacting outcomes significantly.
Methods: From 2011 to 2023, a total of 839 right graft living donor hepatectomies were performed, with 414 (49%) performed via the open approach and 425 (51%) utilizing the robotic platform.
Objective: The aim of this study was to evaluate the impact of robotic techniques on organ transplantation outcomes.
Background: The evolution of organ transplantation is becoming influenced by the adoption of minimally invasive techniques, transitioning from laparoscopic to robotic methods. Robotic surgery has emerged as a significant advancement, providing superior precision and outcomes compared with traditional approaches.
Article Synopsis
- The study evaluates ChatGPT's ability to use the Clavien-Dindo classification (CDC) for grading postoperative complications through AI and Natural Language Processing (NLP).
- ChatGPT 4 outperformed its predecessor in accurately defining the CDC and generating clinical examples, achieving 99% agreement with minor errors, and 97% accuracy for single complications.
- The results indicate that ChatGPT 4 can effectively extract and analyze complications from both fictional and real-world discharge summaries, suggesting it could be a valuable tool in clinical settings for capturing and analyzing CDC data in the future.