Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Purpose: One of the challenges in performing surgery on obese patients with rectal cancer is the prolonged operation time. This study investigates whether or not this issue can be overcome through the surgeon's learning curve as they become proficient in robotic surgery.
Methods: A retrospective review of 396 consecutive robotic procedures. The cases were divided into a learning phase (LP) group (first 20 cases) and a stabilized phase (SP) group (from case 21 onward). Patients were divided into obese (BMI ≥ 25 kg/m) and non-obese groups using 1:1 propensity score matching. This resulted in 130 and 72 patients in the LP and SP groups, respectively. The primary endpoint of this study was operation time.
Results: In the LP group, obese patients had significantly longer operative times (329 min vs. 289 min) and greater blood loss (10 g [0-50] vs. 10 g [0-12]) than non-obese patients. In the SP group, the perioperative outcomes, including operation time, were similar between the two patient groups.
Conclusion: This study suggests that during the early phase of the learning curve, operation time may be prolonged in obese patients. However, once the learning curve stabilizes, the issue of prolonged operative time can be overcome.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00595-025-03113-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Preoperatively predicting failure to achieve the minimum clinically important difference and substantial clinical benefit for total knee arthroplasty patients using machine learning.
Knee 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 PDFPhenotypic age acceleration as a novel predictor of benign prostatic hyperplasia: a prospective cohort study.
Geroscience
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 PDFMulti-class machine learning-based classification of SCID-related genetic variants.
Immunol 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.
Graded nomograms based on perioperative parameters for predicting New-Onset severe acute kidney injury following liver transplantation in patients with normal preoperative renal function: the SALT scale.
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.
View Article and Find Full Text PDFInvestigating Learning Effects Through the Implementation of Teledermatology Consultations Among General Practitioners in Germany: Mixed Methods Process Evaluation.
JMIR Med Educ
September 2025
Institute for General Practice and Interprofessional Care, University Hospital Tübingen, Osianderstraße 5, Tübingen, 72076, Germany, 49 70712985285.
Background: The increasing prevalence of dermatological diseases will pose a growing challenge to the health care system and, in particular, to general practitioners (GPs) as the first point of contact for these patients. In many countries, primary care physicians are supported by teledermatology services.
Objective: The aim of this study was to detect learning effects and gains among GPs through teledermatology consultations (TCs) in daily practice.