JAMA network open
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Observational Study
Trends in Race/Ethnicity Among Applicants and Matriculants to US Surgical Specialties, 2010-2018.
Surgical programs across the US continue to promote and invest in initiatives aimed at improving racial/ethnic diversity, but whether this translates to changes in the percentage of applicants or matriculants from racial/ethnic minority groups remains unclear. ⋯ In this cross-sectional study, overall US surgical programs had no change in the percentage of applicants or matriculants who self-identified as underrepresented in medicine based on race/ethnicity, but the proportion remained higher than in nonsurgical specialties. Reevaluation of current strategies aimed at increasing racial/ethnic representation appear to be necessary to help close the existing gap in medicine and recruit a more racially/ethnically diverse surgical workforce.
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Little is known about how US clinicians have responded to resource limitation during the coronavirus disease 2019 (COVID-19) pandemic. ⋯ The findings of this qualitative study highlighted the complexity of providing high-quality care for patients during the COVID-19 pandemic. Expanding the scope of institutional planning to address resource limitation challenges that can arise long before declarations of crisis capacity may help to support frontline clinicians, promote equity, and optimize care as the pandemic evolves.
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Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features. ⋯ These findings suggest that machine learning models may identify patients with intermediate risk who could benefit from chemoradiation. These models predicted that approximately half of such patients have no added benefit from chemotherapy.
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Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use. ⋯ In this study, simple machine learning techniques performed as well as the more advanced ensemble gradient boosting. Using the clinical variables identified from simple machine learning in a cirrhosis mortality model produced a new score more transparent than machine learning and more predictive than the MELD-Na score.
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United States primary school closures during the 2020 coronavirus disease 2019 (COVID-19) pandemic affected millions of children, with little understanding of the potential health outcomes associated with educational disruption. ⋯ In this decision analytical model of years of life potentially lost under differing conditions of school closure, the analysis favored schools remaining open. Future decisions regarding school closures during the pandemic should consider the association between educational disruption and decreased expected lifespan and give greater weight to the potential outcomes of school closure on children's health.