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Continuing medical education (CME) interventions often evaluate participant commitment to change (CTC) clinical practice. Evidence linking CTC to actual practice change is limited.

In an intervention that combined live CME with changes to the electronic health record to promote judicious antibiotic use for children with urinary tract infections (UTIs), we evaluated CTC and subsequent prescribing behavior in Kaiser Permanente Colorado, an integrated health care system. CTC was assessed immediately after the session using closed-ended questions about session learning objectives and open-ended questions to elicit specific practice changes. Perceived barriers to implementing recommended changes were also assessed.

Among 179 participants, 80 (45%) completed postsession evaluations and treated one or more child with a UTI in the subsequent 17 months (856 UTIs in total). In closed-ended responses about session learning objectives, 45 clinicians (56%) committed to changing practice for antibiotic choice and durant tool for CME evaluators.

Academic presentations in health professions continuing professional development (CPD) often begin with a declaration of real or potential conflicts utilizing a three-slide template or a similar standardized display. These declarations are required in some constituencies. The three-slide template and similar protocols exist to assure learners that the content that follows has been screened, is notionally bias free, and without financial or other influence that might negatively affect health provider behavior. We suggest that there is a potential problem with this type of process that typically focusses in on a narrow definition of conflict of interest. There is the possibility that it does little to confront the issue that bias is a much larger concept and that many forms of bias beyond financial conflict of interest can have devastating effects on patient care and the health of communities. In this article, we hope to open a dialogue around this issue by "making the familiar strange," by asking education o and biases that could affect CPD learners as one dimension of harnessing the power of education to decrease structural inequities.

Bronchiectasis is a chronic disease characterized by an irreversible dilatation of bronchi leading to chronic infection, airway inflammation, and progressive lung damage. Three specific patterns of bronchiectasis are distinguished in clinical practice cylindrical, varicose, and cystic. The predominance and the extension of the type of bronchiectasis provide important clinical information. However, characterization is often challenging and is subject to high interobserver variability. The aim of this study is to provide an automatic tool for the detection and classification of bronchiectasis through convolutional neural networks.

Two distinct approaches were adopted (i) direct network performing a multilabel classification of 32×32 regions of interest (ROIs) into 4 classes healthy, cylindrical, cystic, and varicose and (ii) a 2-network serial approach, where the first network performed a binary classification between normal tissue and bronchiectasis and the second one classified the ROIs containing abnormaribution of bronchiectasis subtype.

The developed networks accurately detect and classify bronchiectasis disease, allowing to collect quantitative information regarding the radiologic severity and the topographical distribution of bronchiectasis subtype.Genetic testing for cardiovascular (CV) disease has had a profound impact on the diagnosis and evaluation of monogenic causes of CV disease, such as hypertrophic and familial cardiomyopathies, long QT syndrome, and familial hypercholesterolemia (FH). The success in genetic testing for monogenic diseases has prompted special interest in utilizing genetic information in the risk assessment of more common diseases such as atherosclerotic cardiovascular disease (ASCVD). Polygenic risk scores (PRS) have been developed to assess the risk of coronary artery disease (CAD) that now include millions of single-nucleotide polymorphisms (SNPs) that have been identified through genome-wide association studies (GWAS). 740 Y-P While these PRS have demonstrated a strong association with CAD in large cross-sectional population studies, there remains intense debate regarding the added value that PRS contribute to existing clinical risk prediction models such as the pooled cohort equations (PCEs). In this review, we provide a brief background of genetic testing for monogenic drivers of CV disease and then focus on the recent developments in genetic risk assessment of ASCVD, including the use of PRS. We outline the genetic testing that is currently available to all cardiologists in the clinic and discuss the evolving sphere of specialized cardiovascular genetics programs (CVGPs) that integrate the expertise of cardiologists, geneticists, and genetic counselors. Finally, we review the possible implications that PRS and pharmacogenomic data may soon have on clinical practice in the care for patients with or at risk of developing ASCVD.The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 has affected the health of people across the globe. Cardiovascular diseases (CVDs) have a significant relationship with COVID-19, both as a risk factor and prognostic indicator, and as a complication of the disease itself. In addition to predisposing to CVD complications, the ongoing pandemic has severely affected the delivery of timely and appropriate care for cardiovascular conditions resulting in increased mortality. The etiology behind the cardiac injury associated with severe acute respiratory syndrome coronavirus-2 is likely varied, including coronary artery disease, microvascular thrombosis, myocarditis, and stress cardiomyopathy. Further large-scale investigations are needed to better determine the underlying mechanism of myocardial infarction and other cardiac injury in COVID-19 patients and to determine the incidence of each type of cardiac injury in this patient population. Telemedicine and remote monitoring technologies can play an important role in optimizing outcomes in patients with established CVD. In this article, we summarize the various impacts that COVID-19 has on the cardiovascular system, including myocardial infarction, myocarditis, stress cardiomyopathy, thrombosis, and stroke.