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The authors then offer 9 recommendations that medical training institutions can implement to critically examine and address racist structures within their organizations to actualize racial equity and justice.

Twenty years have passed since the Liaison Committee on Medical Education (LCME) mandated cultural competence training at U.S. medical schools. There remain multiple challenges to implementation of this training, including curricular constraints, varying interpretations of cultural competence, and evidence supporting the efficacy of such training. This study explored how medical schools have worked to implement cultural competence training.

Fifteen regionally diverse public and private U.S. medical schools participated in the study. In 2012-2014, the authors conducted 125 interviews with 52 administrators, 51 faculty or staff members, and 22 third- and fourth-year medical students, along with 29 focus groups with an additional 196 medical students. Interviews were recorded, transcribed, and imported into NVivo 10 software for qualitative data analysis. Queries captured topics related to students' preparedness to work with diverse patients, engagement with sociocultural issues, and general perception of prntify and address gaps. While LCME standards have transformed aspects of medical education, further research is needed to clarify evidence-based, effective approaches to this training.

There is variation in how medical schools approach cultural competence. Among the 15 participating schools, longitudinal and experiential learning emerged as important, highlighting the needs beyond mere integration of cultural competence content into the formal curriculum. To determine efficacy of cultural competence programming, it is critical to conduct systematic assessment to identify and address gaps. While LCME standards have transformed aspects of medical education, further research is needed to clarify evidence-based, effective approaches to this training.

Developing medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasible for this purpose because they are time-intensive to score. This study explored the potential of using machine learning technologies to score one such assessment-the diagnostic justification essay.

From May to September 2018, machine scoring algorithms were trained to score a sample of 700 diagnostic justification essays written by 414 third-year medical students from the Southern Illinois University School of Medicine classes of 2012-2017. The algorithms applied semantically based natural language processing metrics (e.g., coherence, readability) to assess essay quality on 4 criteria (differential diagnosis, recognition and use of findings, workup, and thought process); the scores for these criteria were summed to create overall scores. Three sources of validity evis. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.

Machine learning technologies may be useful for assessing medical students' long-form written clinical reasoning. Semantically based machine scoring may capture the communicative aspects of clinical reasoning better than faculty ratings, offering the potential for automated assessment that generalizes to the workplace. These results underscore the potential of machine scoring to capture an aspect of clinical reasoning performance that is difficult to assess with traditional analytic scoring methods. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.Contrast-enhanced computed tomography (CT) contributes to the increasing detection of pancreatic neuroendocrine neoplasms (PNENs). Nevertheless, its value for differentiating pathological tumor grades is not well recognized. Bcl-2 inhibitor In this report, we have conducted a retrospective study on the relationship between the 2017 World Health Organization (WHO) classification and CT imaging features in 94 patients. Most of the investigated features eventually provided statistically significant indicators for discerning PNENs G3 from PNENs G1/G2, including tumor size, shape, margin, heterogeneity, intratumoral blood vessels, vascular invasion, enhancement pattern in both contrast phases, enhancement degree in both phases, tumor-to-pancreas contrast ratio in both phases, common bile duct dilatation, lymph node metastases, and liver metastases. Ill-defined tumor margin was an independent predictor for PNENs G3 with the highest area under the curve (AUC) of 0.906 in the multivariable logistic regression and receiver operating characteristic curve analysis. The portal enhancement ratio (PER) was shown the highest AUC of 0.855 in terms of quantitative features. Our data suggest that the traditional contrast-enhanced CT still plays a vital role in differentiation of tumor grades and heterogeneity analysis prior to treatment.We examine how operational changes in customer flows in retail stores affect the rate of COVID-19 transmission. We combine a model of customer movement with two models of disease transmission direct exposure when two customers are in close proximity and wake exposure when one customer is in the airflow behind another customer. We find that the effectiveness of some operational interventions is sensitive to the primary mode of transmission. Restricting customer flow to one-way movement is highly effective if direct exposure is the dominant mode of transmission. In particular, the rate of direct transmission under full compliance with one-way movement is less than one-third the rate under two-way movement. Directing customers to follow one-way flow, however, is not effective if wake exposure dominates. We find that two other interventions-reducing the speed variance of customers and throughput control-can be effective whether direct or wake transmission is dominant. We also examine the trade-off between customer throughput and the risk of infection to customers, and we show how the optimal throughput rate drops rapidly as the population prevalence rises.