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Trends in EMS encounters and outcomes among children and adolescents with firearm injuries from 2018 to 2022.
Am J Emerg Med
January 2025
ESO, Inc, Austin, TX, United States of America.
Objective: To describe changes in patient and encounter characteristics among Emergency Medical Services (EMS) responses for patients ages 0-19 with firearm-related injuries.
Methods: This retrospective national analysis used data from the 2018-2022 ESO Data Collaborative and included all 9-1-1 records for patients ages 0-19 years with documentation of firearm-related injuries. Percent changes are reported; annual changes were evaluated using a non-parametric test of trend.
Preliminary study of gunshot residues in entry holes at different angles: Feasibility of using LIBS to support trajectory estimation.
Forensic Sci Int
December 2024
Ballistics Section of the Spanish Scientific Police Headquarters (National Police), Julián González Segador s/n, Madrid, Spain; Instituto Universitario de Investigación en Ciencias Policiales (IUICP), Universidad de Alcalá, Alcalá de Henares, Madrid, Spain.
Firearm-related scenarios can be highly complex, involving multiple shooters, firearms, types of ammunition, victims, and various impact zones. Obtaining the maximum amount of information to connect each piece of the puzzle is crucial for resolving these cases. Currently, new tools are being developed in the forensic field that facilitate both fieldwork and laboratory analysis, enabling the estimation of trajectories, identification of shooters, and more.
View Article and Find Full Text PDFForecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.
J Surg Res
December 2024
Department of Pediatric Surgery, SSM Health Cardinal Glennon Children's Hospital, St. Louis, Missouri; Department of Pediatric Surgery, St. Louis University, St. Louis, Missouri.
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.
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