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IntroductionThere are no existing data on whether performance on the United States Medical Licensing Examination (USMLE) predicts success in American Board of Emergency Medicine (ABEM) certification. The aim of this study was to determine the presence of any association between USMLE scores and first-time success on the ABEM qualifying and oral certification examinations.MethodsWe retrospectively collected USMLE Step 1, Step 2 Clinical Knowledge (CK) scores and pass/fail results from the first attempt at ABEM qualifying and oral examinations from residents graduating between 2009 and 2011 from nine EM programs. A composite score was defined as the sum of USMLE Step 1 and Step 2 CK scores.ResultsSample was composed of 197 residents. Median Step 1, Step 2 CK and composite scores were 218 ([IQR] 207–232), 228 (IQR 217–239) and 444 (IQR 427–468). First-time pass rates were 95% for the qualifying examination and 93% for both parts of the examination. Step 2 CK and composite scores were better predictors of achieving ABEM initial certification compared to Step 1 score (area under the curve 0.800, 0.759 and 0.656). Step 1 score of 227, Step 2 CK score of 225 and composite score of 444 predicted a 95% chance of passing both boards.ConclusionHigher USMLE Step 1, Step 2 CK and composite scores are associated with better performance on ABEM examinations, with Step 2 CK being the strongest predictor. Cutoff scores for USMLE Step 1, Step 2 CK and composite score were established to predict first-time success on ABEM initial certification.

Coronavirus Disease 2019 (COVID-19) pneumonia has become a global pandemic. Although the rate of new infections in China has decreased, currently, 169 countries report confirmed cases, with many nations showing increasing numbers daily. Testing for COVID-19 infection is performed via reverse transcriptase polymerase chain reaction, but availability is limited in many parts of the world. The role of chest computed tomography is yet to be determined and may vary depending on the local prevalence of disease and availability of laboratory testing. A common but nonspecific pattern of disease with a somewhat predictable progression is seen in patients with COVID-19. Specifically, patchy ground-glass opacities in the periphery of the lower lungs may be present initially, eventually undergoing coalescence, consolidation, and organization, and ultimately showing features of fibrosis. In this article, we review the computed tomography features of COVID-19 infection. Familiarity with these findings and their evolution will help radiologists recognize potential COVID-19 and recognize the significant overlap with other causes of acute lung injury.

The strict application of the NEXUS low-risk criteria could potentially reduce the number of screening cervical spine CT examinations in the setting of trauma in more than 20% of cases, thereby avoiding a significant amount of unnecessary radiation and significant cost.

Subjects observed and reproduced abstract, irregular stimulus models generated by the steady movement of a disk across two-dimensional paths. The paths comprised 3 to 7 randomly oriented linear segments linked head-to-foot. Reproductions were expressed by moving a stylus over the surface of a graphics tablet while the disk was tracing its trajectory (concurrent reproduction), or soon after the disk had finished (delayed reproduction). For both concurrent and delayed conditions, fidelity of reproduction fell with increasing number of segments in the model. Overall quality of reproduction did not differ between the two conditions. When a few models were repeated, interspersed among non-repeated ones, performance improved but only when reproduction was delayed. This improvement was stimulus-selective, not a general improvement with practice. Two additional experiments showed that (i) memory for a seen model is well preserved for at least 6 s, with relatively modest need for rehearsal, and (ii) successful reproduction is possible with remarkably little information having been extracted from key points in the model's trajectory.

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