Military medicine
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Pragmatic Clinical Trial
Development and Implementation of the Military Treatment Facility Engagement Committee (MTFEC) to Support Pragmatic Clinical Trials in the Military Health System.
Within the population of military service members and veterans, chronic pain is highly prevalent, often complex, and frequently related to traumatic experiences that are more likely to occur to members of this demographic, such as individuals with traumatic brain injury or limb loss. In September 2017, the National Institutes of Health (NIH), Department of Defense (DOD), and Department of Veterans Affairs (VA) Pain Management Collaboratory (PMC) was formed as a significant and innovative inter-government agency partnership to support a multicomponent research initiative focusing on nonpharmacological approaches for pain management addressing the needs of service members, their dependents, and veterans. ⋯ Considering the importance of enacting large-scale, pragmatic studies to implement effective strategies in clinical practice for chronic pain management, the MTFEC has begun to actualize its purpose by identifying potential barriers and challenges to study implementation and exploring how the PMC can support and aid in the execution of PCTs by applying similar approaches to stakeholder and subject matter engagement for their research.
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An anonymous online survey was presented to active duty U.S. Army, Reserve, and National Guard Soldiers with experience as en route care medical providers with the intent of identifying factors which contribute to musculoskeletal disorders in U.S. Army en route care medical providers. The survey looked at transport vehicle design, equipment, and awkward postures that could play a role in causing injuries. ⋯ Results of this survey emphasize the need for injury mitigation and prevention strategies to reduce impacts on soldier health and readiness.
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During training and combat operations, military personnel may be exposed to repetitive low-level blast while using explosives to gain entry or by firing heavy weapon systems such as recoilless weapons and high-caliber sniper rifles. This repeated exposure, even within allowable limits, has been associated with cognitive deficits similar to that of accidental and sports concussion such as delayed verbal memory, visual-spatial memory, and executive function. This article presents a novel framework for accurate calculation of the human body blast exposure in military heavy weapon training scenarios using data from the free-field and warfighter wearable pressure sensors. ⋯ This framework has numerous advantages including easier model setup and shorter simulation times. The framework is an important step towards developing an advanced field-applicable technology to monitor low-level blast exposure during heavy weapon military training and combat scenarios.
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The purpose of this pilot study was to obtain preliminary data to culturally adapt the Veteran Health Administration Traumatic Brain Injury (TBI) assessment instruments for the Hispanic Veteran population. A qualitative analysis explored the cognitive processes used by Hispanic Veterans whose preferred language was Spanish to understand a specific set of screening questions within the Initial TBI Screening, the Comprehensive TBI Evaluation, the Neurobehavioral Symptom Inventory (NSI), and the La Trobe Communication Questionnaire (LTCQ). ⋯ Current findings highlight the importance of using linguistically and culturally appropriate materials upon evaluating Hispanic Veterans with a suspected TBI who have Spanish as their primary or preferred language.
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The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension. ⋯ In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.