Scand J Trauma Resus
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Scand J Trauma Resus · Jan 2024
Clinical features and outcomes of orthopaedic injuries after the kahramanmaraş earthquake: a retrospective study from a hospital located in the affected region.
The purpose of this retrospective, single-institutional study was to report the clinical features and outcomes of orthopaedic injuries after the Kahramanmaraş earthquake. ⋯ Fasciotomy appears to be a crucial surgical procedure for the care of earthquake causalities. Fasciotomy can be safely performed as a bedside procedure based on the urgency of the patient's condition as well as the availability of the operating theatre.
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Scand J Trauma Resus · Jan 2024
ReviewUnmanned aerial vehicles and pre-hospital emergency medicine.
Unmanned aerial vehicles (UAVs) are used in many industrial and commercial roles and have an increasing number of medical applications. This article reviews the characteristics of UAVs and their current applications in pre-hospital emergency medicine. The key roles are transport of equipment and medications and potentially passengers to or from a scene and the use of cameras to observe or communicate with remote scenes. The potential hazards of UAVs both deliberate or accidental are also discussed.
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Scand J Trauma Resus · Jan 2024
Randomized Controlled TrialThe SEE-IT Trial: emergency medical services Streaming Enabled Evaluation In Trauma: a feasibility randomised controlled trial.
Use of bystander video livestreaming from scene to Emergency Medical Services (EMS) is becoming increasingly common to aid decision making about the resources required. Possible benefits include earlier, more appropriate dispatch and clinical and financial gains, but evidence is sparse. ⋯ Progression to a definitive RCT is supported by these findings. Bystander video livestreaming from scene is feasible to implement, acceptable to both 999 callers and dispatchers, and may aid dispatch decision-making. Further assessment of unintended consequences, benefits and harm is required.
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Scand J Trauma Resus · Jan 2024
Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.
Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology. ⋯ This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.