45 research outputs found
Prescription of secondary preventive drugs after ischemic stroke: results from the Malaysian National Stroke Registry
A Prospective Multicenter Study Evaluating Learning Curves and Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography Among Advanced Endoscopy Trainees: The Rapid Assessment of Trainee Endoscopy Skills (RATES) Study
Background and aims
Based on the Next Accreditation System, trainee assessment should occur on a continuous basis with individualized feedback. We aimed to validate endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) learning curves among advanced endoscopy trainees (AETs) using a large national sample of training programs and to develop a centralized database that allows assessment of performance in relation to peers.
Methods
ASGE recognized training programs were invited to participate and AETs were graded on ERCP and EUS exams using a validated competency assessment tool that assesses technical and cognitive competence in a continuous fashion. Grading for each skill was done using a 4-point scoring system and a comprehensive data collection and reporting system was built to create learning curves using cumulative sum analysis. Individual results and benchmarking to peers were shared with AETs and trainers quarterly.
Results
Of the 62 programs invited, 20 programs and 22 AETs participated in this study. At the end of training, median number of EUS and ERCP performed/AET was 300 (range 155-650) and 350 (125-500). Overall, 3786 exams were graded (EUS:1137; ERCP–biliary 2280, pancreatic 369). Learning curves for individual endpoints, and overall technical/cognitive aspects in EUS and ERCP demonstrated substantial variability and were successfully shared with all programs. The majority of trainees achieved overall technical (EUS: 82%; ERCP: 60%) and cognitive (EUS: 76%; ERCP: 100%) competence at conclusion of training.
Conclusions
These results demonstrate the feasibility of establishing a centralized database to report individualized learning curves and confirm the substantial variability in time to achieve competence among AETs in EUS and ERCP
Hand Written Digits Recognition Using Digital Learning Networks
AbstractA pattern recognition system based on the n-tuple technique is developed and evaluated for use in classifying non-deterministic data with particular reference to unconstrained hand-written numerals.The system presented in this research fulfils the requirements of simplicity and efficiency making it attractive to practical use in present day industrial environments. This simplicity of operation is afforded by the self evolving nature of the classifier since it is based or a training phase where the recognition logic is developed.Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The results are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces weights into the RAM net is reported