Evaluating Fellowship Training for Endobronchial Ultrasound-Transbronchial Needle Aspiration (EBUS-TBNA) Using the EBUS-Skills and Task Assessment Tool (STAT) and High-Fidelity Bronchoscopy Simulator.

Background: There are two approaches for endobronchial ultrasound (EBUS) training: the traditional apprenticeship approach involving 'see one, do one, teach one', and the computer-based simulation approach. In the traditional approach, the trainee learns under direct supervision from an expert preceptor while performing on patients. In the latter approach, trainees use a high-fidelity bronchoscopy simulator, undertake a skills assessment exam (Endobronchial Ultrasound Skills and Task Assessment Tool (EBUS-STAT)), and receive supervised patient-based training from experienced clinicians.

Long-Acting Reversible Contraception Knowledge Among Female Adolescents Presenting to a Pediatric Emergency Department.

Objectives: The objective of this study was to assess awareness and use of long-acting reversible contraception (LARC) among female adolescents presenting to a pediatric emergency department (PED).

Study Design: During routine presentation to an urban PED in New Jersey, female adolescents, aged 15-19 years, were asked to voluntarily complete an electronic survey about sexual practices and contraception. The PED is in an urban teaching hospital, treating 35,000 children annually.

Clinical Factors That Affect Fiducial Tracking in Robotic SABR for Lung Tumors.

Purpose: SABR is a treatment option for patients with lung tumors that employs fiducials to track tumors during the breathing cycle. Currently, there is a paucity of data on how relative fiducial location and patient clinical characteristics affect fiducial tracking and clinical outcomes. This study aimed to identify factors that reduce the number of fiducials tracked with respiratory motion management during SABR.

Student of Games: A unified learning algorithm for both perfect and imperfect information games.

Games have a long history as benchmarks for progress in artificial intelligence. Approaches using search and learning produced strong performance across many perfect information games, and approaches using game-theoretic reasoning and learning demonstrated strong performance for specific imperfect information poker variants. We introduce Student of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning.