Learning Curve: Part 1 Online Free __HOT__
In this study, we compared the surgical outcomes of ARH and RRH for cervical cancer. In particular, RRH was analyzed by dividing the cohort according to the year of surgery into the first half (RRH1) and the latter half (RRH2). Interestingly, classification according to the year of performance, which is thought to reflect the institutional learning curve, was found to be a significant PFS predictor along with known factors such as tumor size.
Learning Curve: Part 1 Online Free
I recently listened to two podcasts by Dave Roberts both of which individually are well worth checking out. The first is on learning curves and their implication for renewable energy policy and the second is on enhanced geothermal power. In both, Roberts interviews authors of recent studies on the respective topics. After listening to them, I wondered how learning curves apply to enhanced geothermal power. To learn more, I dug into the studies covered in the podcasts and then turned to my colleague Yeh-Tang Huang to get the latest on geothermal. This, then, is the second in a two-part series on learning curves, enhanced geothermal power and speculating about what learning curves can tell us about the future of the cost of enhanced geothermal power.
Enhanced geothermal power has been a potentially zero-carbon source of electricity for a little over two decades, but it might finally be ready to deliver on its promise. In fact, a recent study covered in another great podcast by Dave Robert suggests that enhanced geothermal systems (EGS) might be flexible in a way that would make them a valuable complement to wind and solar. How big a role enhanced geothermal systems (EGS) play in a clean energy future will depend on whether its costs follow a learning curve similar to wind and solar or one more like oil and gas. (For more on learning curves, see part 1 of this series.)
I recently listened to two podcasts by Dave Roberts: the first is on learning curves and their implication for renewable energy policy and the second is on enhanced geothermal power. This is the first in a two-part series on learning curves, enhanced geothermal power and speculating about what learning curves can tell us about the future of the cost of enhanced geothermal power.
Stone clearance is a very important clinical indicator of competency. In a study by Tanriverdi [6],the stone-clearance rate did not show any significant changes in sequential groups of patients. The authors concluded that a surgeon can meet this goal very soon after completing the first few procedures. We found high stone-free and CIRF rates for the novice surgeon that reached almost 95%, which suggests that the complete removal of all stones can be reliably achieved while the surgeon is learning the procedure. However, to achieve complete stone clearance, secondary treatments were sometimes needed. Although the stone-free rate increased with more practice, the difference was not significant.
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