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Advances in surgical techniques and technologies require surgeons to constantly retrain and adopt these into their practices. Until the surgeon gains proficiency in the new technique, he is said to be on the learning curve.
Sanjay Rao
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Learning curve in autofluorescence-guided thyroid surgery [PDF]
BackgroundNear-infrared autofluorescence (NIRAF) has been introduced as an adjunct to improve parathyroid gland (PG) identification during thyroid surgery.
Ali Abood +3 more
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Learning Curve, Survival Curve [PDF]
Reena George +5 more
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Teaching a culture of care: Why it matters
The use of animal in biomedical research remains a critical compromise. Research and higher education institutions play a major role in educating on the use of animal and such training is expected to translate into the development of a culture of care ...
Jordi López Tremoleda, Angela Kerton
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The Fallacy of the “Learning Curve”
John T. Campbell MD +7 more
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Learning curve for arteriovenous fistula creation
Objective: Amount of time and number of procedures required in junior surgeon (JS) to achieve arteriovenous fistula (AVF) patency rate of surgeon with 20 years of experience.
Ivan Neretljak +3 more
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ON THE FORM OF LEARNING CURVES [PDF]
In a wide variety of learning experiments, cumulated measures of performance turn out to be a power function of practice time or number trials, typically with an exponent between 1 and 2. The effects of various conditions on the course of learning can frequently be described in terms of the two constants of the function.
J C, STEVENS, H B, SAVIN
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Bridgewater and colleagues have studied the surgical results of surgeons in each of their first four years of independent practice and report that there is a learning curve.1 To explain the concept of a learning curve a surgeon writing in the New Yorker magazine chose for his example the insertion of a central venous line into the subclavian vein by ...
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Learning to Rank Learning Curves
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to
Martin Wistuba, Tejaswini Pedapati
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