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Background: Limited and heterogeneous literature data necessitate a focused examination of the learning curve in robotic liver resections. This study aims to assess the learning curve of two surgeons from the same team with differing laparoscopic backgrounds.
Methods: Since February 2021, San Raffaele Hospital in Milan has implemented a robotic liver surgery program, performing 250 resections by three trained console surgeons. Using cumulative sum (CUSUM) analysis, the learning curve was evaluated for a Pioneer Surgeon (PS) with around 1200 laparoscopic cases and a New Generation Surgeon (NGS) with approximately 100 laparoscopic cases. Cases were stratified by complexity (38 low, 74 intermediate, 85 high).
Results: Both PS and NGS demonstrated a learning curve for operative time after 15 low-complexity and 10 intermediate-complexity cases, with high-complexity learning curves apparent after 10 cases for PS and 18 cases for NGS. Conversion rates remained unaffected, and neither surgeon experienced increased blood loss or postoperative complications. A "team learning curve" effect in terms of operative time emerged after 12 cases, suggesting the importance of a cohesive surgical team.
Conclusion: The robotic platform facilitated a relatively brief learning curve for low and intermediate complexity cases, irrespective of laparoscopic background, underscoring the benefits of team collaboration.
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http://dx.doi.org/10.1016/j.hpb.2024.10.007 | DOI Listing |
Multimed Man Cardiothorac Surg
September 2025
Robotic mitral repair is often associated with longer ischaemic and cardiopulmonary bypass times, particularly early in the learning curve. We demonstrate a semi-continuous, three-suture technique for robotic annuloplasty that retains the mechanical principles of traditional interrupted sutures while leveraging the advantages of robotic precision and exposure. The use of pre-knotted sutures minimizes intra-cardiac knot tying, further enhancing procedural efficiency.
View Article and Find Full Text PDFKnee Surg Relat Res
September 2025
Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
This study aims to investigate the predictive value of combined phenotypic age and phenotypic age acceleration (PhenoAgeAccel) for benign prostatic hyperplasia (BPH) and develop a machine learning-based risk prediction model to inform precision prevention and clinical management strategies. The study analyzed data from 784 male participants in the US National Health and Nutrition Examination Survey (NHANES, 2001-2008). Phenotypic age was derived from chronological age and nine serum biomarkers.
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
Ren Fail
December 2025
Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China.
This study aimed to develop a predictive model and construct a graded nomogram to estimate the risk of severe acute kidney injury (AKI) in patients without preexisting kidney dysfunction undergoing liver transplantation (LT). Patients undergoing LT between January 2022 and June 2023 were prospectively screened. Severe AKI was defined as Kidney Disease: Improving Global Outcomes stage 3.
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