Purpose: To assess determinants of performance on the Transfer Task of the Basic Laparoscopic Urologic Surgery (BLUS(©)) skills curriculum administered at Objective Structured Clinical Examinations (OSCEs).
Methods: After obtaining Institutional Review Board approval and informed consent, urology trainees (Postgraduate Year [PGY]-3 to PGY-5) from four different training programs (A, B, C, D) were recruited for the study. Transfer Task Times (TTTs) were compared and correlated with previous laparoscopic experience, amount of endotrainer practice and scores obtained at practice sessions and other OSCE stations.
Results: A total of 37 trainees were evaluated on three successive semiannual OSCEs from May 2011 to May 2012, including 16 (43.2%) trainees from program A with a dedicated laparoscopic skills training program. Compared with trainees from programs B, C, and D, trainees from program A had significantly more practice per week (0 v 45 minutes, p=0.001) and significantly lower median TTTs at OSCEs (114 [68-209] v 74 [52-189] seconds, p=0.001) despite significantly lower number of laparoscopic cases assisted within the previous 6 months (13 [0-57] v 2 [0-35], p=0.001). For program A trainees, TTTs moderately correlated with median TTTs at practice sessions (r=0.57, p=0.001) and negatively correlated with amount of practice per week (r=-0.41, p=0.003). Thus, more training resulted in faster times at OSCEs. On multivariate analysis, amount of practice per week was the only significant predictor of TTTs at OSCEs (p=0.028).
Conclusion: Performance on the transfer task of BLUS during OSCEs significantly correlated with the amount of practice rather than the number of laparoscopic cases assisted.
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http://dx.doi.org/10.1089/end.2013.0065 | DOI Listing |
Sensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
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December 2024
Széchenyi István University, 9026 Győr, Hungary.
Over the past twenty years, camera networks have become increasingly popular. In response to various demands imposed on these networks, several coverage models have been developed in the scientific literature, such as area, trap, barrier, and target coverage. In this paper, a new type of coverage task, the Maximum Target Coverage with k-Barrier Coverage (MTCBC-k) problem, is defined.
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December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
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December 2024
AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea.
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.
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January 2025
Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
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