Navigating Communication in Racially Concordant Care: Considerations for Medical Education.

Background And Objectives: Black/African American medical professionals and students engage in patient-centered communication in ways that are not yet described in medical education literature. The purpose of this paper is to explore the ways in which Black/African American attending physicians, residents, and medical students enact patient-centered communication while interacting with their Black/African American patients.

Methods: Forty-one Black/African American attending physicians, residents, and medical students were recruited through a snowball sample of the authors' personal and professional networks.

Interactions between leaf phenological type and functional traits drive variation in isoprene emissions in central Amazon forest trees.

The Amazon forest is the largest source of isoprene emissions, and the seasonal pattern of leaf-out phenology in this forest has been indicated as an important driver of seasonal variation in emissions. Still, it is unclear how emissions vary between different leaf phenological types in this forest. To evaluate the influence of leaf phenological type over isoprene emissions, we measured leaf-level isoprene emission capacity and leaf functional traits for 175 trees from 124 species of angiosperms distributed among brevideciduous and evergreen trees in a central Amazon forest.

Five year mortality in an RCT of a lung cancer biomarker to select people for low dose CT screening.

The role of biomarkers in risk-based early detection of lung cancer may enable screening to become cost effective and widely accessible. EarlyCDT-Lung is an example of such a blood-based autoantibody biomarker which may improve accessibility to Low dose Computed Tomography (LDCT) screening for those at highest risk. We randomized 12 208 individuals aged 50-75 at high risk of developing lung cancer to either the test or to standard clinical care.

Forecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.

Introduction: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource management and preparedness, particularly if daily forecasts are achievable. The aim of the study is to evaluate various machine learning models' accuracy on monthly, weekly, and daily data.