Purpose: The learning curve for robot-assisted partial nephrectomy (RAPN) has not been extensively studied. We therefore evaluated the learning curve of RAPN for a fellowship-trained laparoscopic surgeon with extensive prior experience with laparoscopic partial nephrectomy (LPN). We also examined the potential effect of tumor size on the learning curve.
Patients And Methods: We prospectively evaluated 38 consecutive patients undergoing RAPN by a single surgeon (S.B.B.). Sixteen patients had tumors <2 cm, and 22 patients had tumors >2 cm. Warm ischemia times and overall operative times were recorded as indices of learning progression.
Results: Average operative time for tumors <2 cm was 131.9 minutes (115.3-148.5 minutes) and for tumors >2 cm was 145.8 minutes (131.1-160.5 minutes). The difference between the operative times for tumors <2 and >2 cm was not statistically significant (p = 0.23). Average warm ischemia time for tumors <2 cm was 21 minutes (16.9-25.1 minutes) and for tumors >2 cm was 24.7 minutes (21.3-28.1 minutes). This difference was also not statistically significant (p = 0.20). Defined by the overall operative time, the learning curve for RAPN was 16 cases, and by ischemic time, the learning curve was 26 cases. Tumor size did not have an effect on the learning curve.
Conclusions: The learning curve for RAPN is short for surgeons already experienced with LPN. The learning curve for portions performed under warm ischemia is slightly longer, implying that the critical portions of the procedure require more experience to become facile. Tumor size does not appear to have a significant impact on the learning curve for surgeons experienced with LPN.
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http://dx.doi.org/10.1089/end.2008.0601 | DOI Listing |
BMC Pulm Med
January 2025
Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.
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January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
February 2025
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. Electronic address:
Background: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.
Methods: Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.
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