Background: The United States lacks equitable surgical access, prompting us to investigate whether there is an inverse relationship between Social Vulnerability Indices and the number of surgeons in a census tract, using the Inland Empire as a model.
Methods: The Centers for Disease Control's (CDC) SVI 2018 database, composed of 823 census tracts, was compared against demographics of 1008 surgeons, from the American Medical Association's (AMA) 2018 Physician Masterfile. Analysis was performed via Spearman's bivariate and multiple regression.
Background: Accurately predicting which patients are most likely to benefit from massive transfusion protocol (MTP) activation may help patients while saving blood products and limiting cost. The purpose of this study is to explore the use of modern machine learning (ML) methods to develop and validate a model that can accurately predict the need for massive blood transfusion (MBT).
Methods: The institutional trauma registry was used to identify all trauma team activation cases between June 2015 and August 2019.