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Poor early urinary outcomes after laparoscopy were reported in studies comparing laparoscopic versus open rectal cancer surgery. One possible explanation was that these studies might include a number of patients on the laparoscopic surgeons' learning curve. This study aims to evaluate whether the learning curve of laparoscopic rectal cancer surgery influences early postoperative urinary dysfunction. Between September 2009 and December 2014, 208 consecutive patients undergoing laparoscopic rectal resection for rectal cancer were enrolled in the present study. All the clinical data were obtained from a prospectively compiled database. The primary outcomes were the incidences of postoperative urinary retention (POUR) and major urinary dysfunction requiring long-term urinary catheterization. POUR and major urinary dysfunction rate were 20.2 per cent (42/208) and 4.3 per cent (9/208), respectively. The learning curve analysis for operative time using the moving average method showed stabilization at 80 cases. Surgeon experience was divided into two periods: learning curve period (1-80 cases) and experienced period (81-208 cases). Multivariate analysis showed that older age (OR = 3.250, P = 0.006) and learning curve (OR = 2.241, P = 0.024) were independent risk factors for POUR. Learning curve was not significantly associated with increased rates of major urinary dysfunction (OR = 3.378, P = 0.092). Learning curve is a significant risk factor for increased rate of POUR after laparoscopic rectal cancer surgery. Technical training may be key to shorten the learning curve and limit its impact on the postoperative urinary complications.
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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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