The Trauma and Injury Severity Score (TRISS) is a well-accepted model used to evaluate the quality of trauma care in the US. This research aims to study whether TRISS can be applied to evaluate trauma care and classify outcomes of road traffic injury patients in Thailand. A retrospective study was used to review the Thailand's Injury Surveillance System database from the 1st January to the 31st of December 1996. The study subjects were severe road traffic injury patients with blunt injuries. The TRISS model was applied to compute the survival probability for each patient. The chi-square goodness-of-fit was used to compare the survival probability distribution between the American Major Trauma Outcome of Study (MTOS) and the road traffic injuries in Thailand. The accuracy, sensitivity and specificity of the survival prediction by TRISS were evaluated. The distribution of survival probability between American trauma patients and Thai road traffic injury patients was significantly different (p-value < 0.00001). The TRISS model had high accuracy and sensitivity, but low specificity, in predicting the survival of Thai road traffic injuries. The MTOS and Thai road traffic injuries had different distributions for various factors such as the Revised Trauma Score (RTS), Injury Severity Score (ISS), and ages which effect injury survival. Due to these factors the distribution of survival probability between MTOS and Thai road traffic injuries was also significantly different. By applying TRISS, the survival prediction of Thai road traffic injuries resulted in a high number of false positives.
Download full-text PDF |
Source |
---|
Accid Anal Prev
January 2025
Western Australian Centre for Road Safety Research, School of Psychological Science, The University of Western Australia Perth Western Australia Australia.
Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering, The University of Mississippi, University, MS 38677, USA.
The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Netcom Engineering S.p.A., Via Nuova Poggioreale, Centro Polifunzionale, Tower 7, 5th Floor, 80143 Naples, Italy.
This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!