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Purpose: We previously described a new procedure specific module (Tube 3) to allow the practice of vesicourethral anastomosis after robot-assisted radical prostatectomy. Herein, we report a predetermined proficiency level of Tube 3 and preliminary validation to explore whether this new module can lead to performance improvement in the da Vinci system.
Materials And Methods: Eight urology residents and three urology fellows performed the Tube 3 module 1 hour daily for 7 days. The learning curve was depicted through a scatterplot and the stable point was identified through the cumulative sum chart. Concurrent and predictive validations were performed with the da Vinci system. The mean time to complete the task and end product rating score between Tube 3 training group and no Tube 3 training group were compared.
Results: Concerning the learning curve, about 41 repetitions comprising about 5 hours were needed to achieve this stable point when the mean time to complete Tube of 384 seconds was set as a target. With regarding to the concurrent and predictive validation, there significant differences were evident in the mean time to complete 16 needle passages and the vesicourethral anastomosis and the end product rating score.
Conclusions: The virtual reality (VR) simulator can yield sufficient improvement in technical performance in Tube 3 within 5 hours. The acquired proficiency can be transferable to the vesicourethral anastomosis using the da Vinci system.
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http://dx.doi.org/10.4111/kju.2015.56.11.756 | DOI Listing |
Obes Surg
September 2025
Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
This rapid systematic review aimed to evaluate the diagnostic accuracy (concurrent validity, predictive ability, reliability) of indirect calorimetry (IC) for measuring resting energy expenditure (REE) in adults with overweight or obesity. PubMed and Web of Science searched for studies measuring REE by IC in adults with overweight or obesity and reported primary outcomes: concurrent validity, predictive ability, or reliability. N = 22 studies were included that evaluated n = 10 IC devices.
View Article and Find Full Text PDFNeurol Res
September 2025
Department of Neurosurgery, Xingtai Central Hospital, Xingtai, China.
Objective: To investigate the prognostic significance of concurrent monitoring of serum S100 calcium-binding protein A12 (S100A12) and optic nerve sheath diameter (ONSD) in patients with traumatic intracranial hematoma.
Methods: This prospective observational study included 198 patients with traumatic intracranial hematoma from Xingtai Central Hospital (February 2022-June 2024). Serum S100A12 and ONSD were measured at admission and postoperatively, and patients received minimally invasive therapy.
Rheumatol Int
September 2025
Division of Rheumatology and Immunology, Department of PMR, , Sakarya University School of Medicine, Sakarya, Turkey.
To identify clinical and demographic predictors associated with the timing of transition from psoriasis (PsO) to psoriatic arthritis (PsA), and to compare the characteristics of patients with concurrent PsO-PsA onset versus those with prolonged transition. A multi-center, observational study was conducted using data from the Turkish League Against Rheumatism (TLAR) network including PsA patients fulfilling CASPAR criteria. Patients were categorized into two groups: Group 1 (concurrent PsO and PsA onset within ± 1 year) and Group 2 (prolonged transition to PsA, > 1 year after PsO).
View Article and Find Full Text PDFBackground: Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in AKI patients with liver cirrhosis using the MIMIC-IV database.
Methods: This retrospective study analyzed data from 4,168 AKI patients, including 601 with concurrent liver cirrhosis, from the MIMIC-IV database.
IEEE Trans Neural Netw Learn Syst
September 2025
Class incremental learning (CIL) offers a promising framework for continuous fault diagnosis (CFD), allowing networks to accumulate knowledge from streaming industrial data and recognize new fault classes. However, current CIL methods assume a balanced data stream, which does not align with the long-tail distribution of fault classes in real industrial scenarios. To fill this gap, this article investigates the impact of long-tail bias in the data stream on the CIL training process through the experimental analysis.
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