Articles: trauma.
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Spinal injuries are difficult injuries to assess yet can be associated with significant neurological damage. To avoid secondary damage, immobilization is considered state of the art trauma care. The indication for spinal immobilization must be assessed, however, for potential complications as well as its advantages and disadvantages. ⋯ High-quality studies demonstrating the benefit of prehospital spinal immobilization are still lacking. Decision tools such as the Immo traffic light system can help weigh up the pros and cons of immobilization.
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Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. ⋯ The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.
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Review Meta Analysis
Current trends in the diagnosis and management of traumatic diaphragmatic injuries: A systematic review and a diagnostic accuracy meta-analysis of blunt trauma.
Traumatic diaphragmatic injuries (TDI) are wounds or ruptures of the diaphragm due to thoraco-abdominal trauma. Nowadays, CT-scan is considered the gold standard for TDI diagnosis. The aim of this study was to assess the current diagnostic accuracy of CT-scan in the diagnosis of TDI and describe the management of this type of injury. ⋯ CT-scan has a good sensitivity and specificity for blunt TDI diagnosis. However, TDI diagnosis and management are often delayed. The use of water-soluble contrast in CT-scan should be considered when the diagnosis of TDI is not defined after the first scan, and clinical suspicion is still high. In this context, a highly trained trauma team is essential for trauma management and correct imaging interpretation.
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Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research. ⋯ This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.
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The first Trauma and Orthopaedic unit dates back to 1780, originally dedicated to the treatment of children's deformities. The specialty has subsequently become multifaceted, with a plethora of subspecialty areas of which orthopaedic trauma is the most commonly practiced. Recently there has been a significant demand for an evidence base with more than 130,000 of the 162,000 publications in the last century occurring within the past 20 years. This narrative review will summarise some of the more landmark changes within orthopaedic trauma that have been made within the past 20 years, whilst also attempting to predict where the specialty will continue to develop as we move forward.