Objective: Despite advances in vehicle safety systems, motor vehicle crashes continue to cause ankle fractures. This study attempts to provide insight into the mechanisms of injury and to identify the at-risk population groups.
Methods: A study was made of ankle fractures patients treated at an urban level 1 trauma center following motor vehicle crashes, with a concurrent analysis of a nationally representative crash data set. The national data set focused on ankle fractures in drivers involved in frontal crashes. Statistical analysis was applied to the national data set to identify factors associated with fracture risk.
Results: Malleolar fractures occurred most frequently in the driver's right foot due to pedal interaction. The majority of complex/open fractures occurred in the left foot due to interaction with the vehicle floor. These fractures occurred in association with a femoral fracture, but their broad injury pattern suggests a range of fracture causation mechanisms. The statistical analysis indicated that the risk of fracture increased with increasing driver body mass index (BMI) and age.
Conclusions: Efforts to reduce the risk of driver ankle injury should focus on right foot and pedal interaction. The range of injury patterns identified here suggest that efforts to minimize driver ankle fracture risk will likely need to consider injury tolerances for flexion, pronation/supination, and axial loading in order to capture the full range of injury mechanisms. In the clinical environment, physicians examining drivers after a frontal crash should consider those who are older or obese or who have severe femoral injury without concurrent head injury as highly suspicious for an ankle injury. 相似文献
The objective of this article is to present a method for developing collision risk indicators applicable for autonomous remotely operated vehicles (AROVs), which are essential for promoting situation awareness in decisions support systems. Three suitable risk based collision indicators are suggested for AROVs namely, time to collision, mean time to collision and mean impact energy. The proposed indicators are classified into different thresholds; low, intermediate and high. An AROV flight path is simulated to gather input data to calculate the proposed indicators and three collision targets are established, i.e., subsea structure, seabed and a cooperating AROV. The proposed indicator development method together with the case study show a proof-of-concept that the combination of mean time to collision and mean impact energy indicators can identify risk prone waypoints in the AROV path. The method results in an overall risk picture for a given AROV path. The results may provide useful input in replanning of mission paths and for implementation of risk reducing measures. Even though the method focuses on collision risk, it can be used for other accident scenarios for AROVs. 相似文献