We report simultaneous multi-frequency observing performance at 22 and 43 GHz of the 21-m shaped-Cassegrain radio telescopes of the Korean VLBI Network (KVN). KVN is the first millimeter-dedicated VLBI network in Korea having a maximum baseline length of 480 km. It currently operates at 22 and 43 GHz and planed to operate in four frequency bands, 22, 43, 86, and 129 GHz. The unique quasioptics of KVN enable simultaneous multi-frequency observations based on efficient beam filtering and accuarate antenna-beam alignment at 22 and 43 GHz. We found that the offset of the beams is within < 5 arcseconds over all pointing directions of antenna. The dual polarization, cooled HEMT receivers at 22 and 43 GHz result in receiver noise temperatures less than 40 K at 21.
With the increased utilization of robot thyroidectomy in recent years, surgical proficiency is the paramount consideration. However, there is no single perfect or ideal method for measuring surgical proficiency. In this study, we evaluated the learning curve of robotic thyroidectomy using various parameters. A total of 172 robotic total thyroidectomies were performed by a single surgeon between March 2014 and February 2018. Cumulative summation analysis revealed that it took 50 cases for the surgeon to significantly improve the operation time. Mean operation time was significantly shorter in the group that included the 51st to the 172nd case, than in the group that included only the first 50 cases (132.8 ± 27.7 min vs. 166.9 ± 29.5 min; p < 0.001). On the other hand, the surgeon was competent after the 75th case when postoperative transient hypoparathyroidism was used as the outcome measure. The incidence of hypoparathyroidism gradually decreased from 52.0%, for the first 75 cases, to 40.2% after the 76th case. These results indicated that the criteria used to assess proficiency greatly influenced the interpretation of the learning curve. Incorporation of the operation time, complications, and oncologic outcomes should be considered in learning curve assessment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.