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Introduction: Polytrauma, to include major limb amputation, in a military population presents unique rehabilitation challenges with the overarching goal of restoring function leading to the primary question, "Is this Service Member (SM) capable of returning to duty following rehabilitation?" The US military has a vested interest in maximizing injured SMs occupational performance to allow for return to duty. The purpose of this report is to describe marksmanship (shot grouping and weapon qualification) and return to duty outcomes following a course of VRE-based firearm training in a polytrauma patient population.
Methods: The medical records, stored in the Armed Forces Health Longitudinal Technology Application (AHLTA), of all patients who received rehabilitative care at the Center for the Intrepid (CFI) to include VRE-based firearms training between 01OCT2015 and 01AUG2016 were manually reviewed for inclusion. Subjects included all adult (18 years and older) SMs (active duty at time of admission) with a diagnosis of polytrauma who had been referred to and treated (received additional services such as physical and or occupational therapy) at the CFI. Approval for this research was received from the Brooke Army Medical Center Department of Clinical Investigation Office of the Institutional Review Board.
Results: Medical records of 30 SMs with a polytrauma diagnosis met the inclusion criteria. Mean shot group sizes for the M9 and M4 weapon decreased between initial and post training time points for the M9 zero (p = 0.009) and M4 zero (p = 0.020). There was no significant difference between initial and post training time points at the other shooting distances with either weapon. There was an 89% qualification rate for both the M9 (n = 18) and M4 (n = 19) weapons for those who attempted qualification; 43% of the population (n = 13) did not attempt qualification with either weapon.
Conclusion: SMs with polytrauma demonstrated a high rate of weapon qualification (accuracy) following VRE-based firearm training. Shot group size (precision) at short distances with a M9 pistol and M4 rifle also improved with training. While overall marksmanship appeared to improve, high return to duty rates were not directly related to firearm training or marksmanship. Future efforts need to focus on consistent clinical documentation of firearm training procedure and the establishment of psychometric properties for marksmanship outcome measures.
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http://dx.doi.org/10.1093/milmed/usz010 | DOI Listing |
JMIR Res Protoc
September 2025
OCHIN, Portland, OR, United States.
Background: Structured data codes capture acute bodily injury from firearm violence but do not necessarily describe follow-up care from bodily injury and secondary exposure to firearm violence (eg, witnessing a shooting, being threatened by a firearm, or losing a loved one to gun violence and injury from firearms) even though such exposure is associated with many short- and long-term health impacts. Clinical notes from electronic health records (EHRs) often contain data not otherwise captured in structured data fields and can be categorized using natural language processing (NLP).
Objective: This study protocol outlines the steps being taken to develop an NLP text classifier for determination of exposure to firearm violence (both primary and secondary exposure) from ambulatory primary care and behavioral health EHR clinical notes for persons aged ≥5 years.
Inj Epidemiol
September 2025
Firearm Injury & Policy Research Program, University of Washington, Seattle, WA, USA.
Background: Youth are at high risk for firearm-related injury and death. However, research combining children and adolescents into one homogeneous group ignores distinct developmental stages and associated risks. Addressing firearm mortality as a public health crisis requires strategies tailored to developmental stage, injury intent, setting, and cultural context.
View Article and Find Full Text PDFTwenty-one states, the District of Columbia, and the U.S. Virgin Islands have passed Extreme Risk Protection Order (ERPO) laws, risk-based firearm removal policies intended to reduce firearm violence.
View Article and Find Full Text PDFDiagnostics (Basel)
August 2025
Department of Medical, Surgical and Advanced Technologies "G.F. Ingrassia", University of Catania, 95121 Catania, Italy.
: The integration of artificial intelligence (AI) into forensic science is expanding, yet its application in firearm injury diagnostics remains underexplored. This study investigates the diagnostic capabilities of ChatGPT-4 (February 2024 update) in classifying gunshot wounds, specifically distinguishing entrance from exit wounds, and evaluates its potential, limitations, and forensic applicability. : ChatGPT-4 was tested using three datasets: (1) 36 firearm injury images from an external database, (2) 40 images of intact skin from the forensic archive of the University of Catania (negative control), and (3) 40 real-case firearm injury images from the same archive.
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