Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

Steep Decrease of Gender Difference in DSM-IV Alcohol Use Disorder: A Comparison of Two Nation-wide Surveys Conducted 10 Years Apart in Korea

While decreasing trend in gender differences in alcohol use disorders was reported in Western countries, the change in Asian countries is unknown. This study aims to explore the shifts in gender difference in alcohol abuse (AA) and dependence (AD) in Korea. We compared the data from two nation-wide community surveys to evaluate gender differences in lifetime AA and AD by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Face-to-face interviews using the Composite International Diagnostic Interview (CIDI) were applied to all subjects in 2001 (n=6,220) and 2011 (n=6,022). Male-to-female ratio of odds was decreased from 6.41 (95% CI, 4.81-8.54) to 4.37 (95% CI, 3.35-5.71) for AA and from 3.75 (95% CI, 2.96-4.75) to 2.40 (95% CI, 1.80-3.19) for AD. Among those aged 18-29, gender gap even became statistically insignificant for AA (OR, 1.59; 95% CI, 0.97-2.63) and AD (OR, 1.18; 95% CI, 0.80-2.41) in 2011. Men generally showed decreased odds for AD (0.55; 95% CI, 0.45-0.67) and women aged 30-39 showed increased odds for AA (2.13; 95% CI 1.18-3.84) in 2011 compared to 2001. Decreased AD in men and increased AA in women seem to contribute to the decrease of gender gap. Increased risk for AA in young women suggests needs for interventions.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.     

Immune checkpoint inhibitor-induced inflammatory arthritis: identification and management

ABSTRACT

Introduction

Immune checkpoint inhibitors (ICIs) have proved to be groundbreaking in the field of oncology. However, immune system overactivation from ICIs has introduced a novel medical entity known as immune-related adverse events (irAEs), that can affect any organ or tissue. ICI-induced inflammatory arthritis (ICI-IIA) is the most common musculoskeletal irAE and can lead to significant morbidity and limitation in anti-cancer therapy.