Characteristics and Outcome of Pediatric in a Referral Trauma Center in Iran; a Cross-Sectional Study

Background Pediatric trauma is the leading cause of death from early childhood through adolescence.In this study, characteristics and associated factors of pediatric trauma cases are evaluated. Methods In this cross-sectional study, demographic and clinical characteristics of 622 patients admitted to a referral hospital in Tehran, Iran are evaluated. Reported clinical characteristics include mechanism of trauma, type of trauma, ultrasonographic (US) findings, chest and abdomino-pelvic and brain computed tomography (CT) scan findings, blood hemoglobin (Hb) level, urinalysis, type of surgery, and mortality rate. Results The average age was 7.46 ± 3.64 years and the majority (63.7%) were male and endured direct trauma (36.3%). Most of our patients (n=305; 49%) were admitted in the orthopedic ward, followed by the neurosurgery ward (n=235; 37.8%). The mortality rate was 23 cases (3.7%). the lowest mortality was in the patients 11 to 15 years old (n=3; 13%), and the highest in five years and younger group (n=14; 60.9%). Conclusions Motor vehicle accidents are the leading fatal trauma injuries in children, therefore implementation of preventive measures specific to children is quite important but long overdue.

Prediction of Bike Share Demand by Machine Learning

In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.

Proposal for Prevention of Drowsy Driving-Related Motor Vehicle Accidents in Patients With Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is known to be associated with various health concerns, including sleepiness, fatigue, cognitive dysfunction, diminished quality of life, hypertension, cardiovascular diseases, and stroke. OSA-induced sleepiness at the wheel reduces vigilance and driving performance, which significantly increase the risk of motor vehicle accidents. Sleepiness-induced motor vehicle accidents are characterized by high morbidity and mortality. OSA is a well-established significant risk factor for drowsy driving-related motor vehicle accidents, which can be prevented through appropriate treatment. However, currently no clinical guidelines or regulations are available for evaluation or management of the risk of motor vehicle accidents in patients with OSA in Korea. In this review, we discuss the risk of motor vehicle accidents in patients with OSA, the effects of positive airway pressure therapy as a preventive measure to reduce this risk, and the published recommendations for OSA in other countries with regard to fitness to drive. We propose recommendations for screening, evaluation, and treatment of OSA with regard to the risk of motor vehicle accidents, which would serve as useful practical guidelines for sleep specialists in clinical practice. Further research is warranted to establish optimal strategies for effective improvements in OSA-related traffic safety.

Proposal for Preventing Motor Vehicle Accident Related to Drowsy Driving in Patients With Narcolepsy

Narcolepsy is a chronic sleep disorder characterized by irresistible sleep attacks, hypersomnolence, cataplexy (sudden loss of muscle tone provoked by emotion), and sleep paralysis. Individuals with narcolepsy are at a high risk of experiencing sleepiness while driving leading to road traffic accidents. To prevent such accidents, some countries have regulations for commercial and noncommercial drivers with narcolepsy. Evaluating sleepiness is essential. Therefore, several subjective reports and objective tests were used to predict the possibility of car crashes or near-misses. Brain stimulants are effective in treating narcolepsy and can reduce daytime sleepiness in these patients. However, no guideline has been established for the driving safety of patients with narcolepsy in Korea. The Korean Sleep Research Society has prepared this proposal for preventing motor vehicle accidents caused by drowsy driving in patients with narcolepsy.

Bilateral Floating Knee Injuries in an Infant: A Case Report and Literature Review

Background: Floating knee injury is defined as ipsilateral fractures of the femur and tibia. It was mostly seen among young men and is generally caused by high-velocity trauma such as motor vehicle accidents and falling from height. Although isolated fractures of the femur or tibia are relatively common in children, floating knee injuries are rare in adolescents and even less frequent in younger children. Case Report: In this study, we reported a case of bilateral floating knee injuries of a 10-month old girl infant. Radiographic examinations revealed diaphyseal fracture of the left femur and proximal metaphyseal fracture of tibia compatible with floating knee injury type B according to the classification proposed by Letts et al. To the best of our knowledge, there was not any previous report of bilateral infantile floating knee injury in the literature. Conclusion: Pediatric floating knee injuries are relatively uncommon and extremely rare among infants. The preferred method of treatment is a surgical fixation for all fractures of all ages.

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

Effects of the COVID-19 Pandemic on Pediatric Nasal Fractures

Objectives Nasal bone fracture is a frequent entity consulted to the otolaryngologists, approximately accounting for 39% of all facial bone fractures. The most frequent mechanisms of injury consist of assault, sport-related injuries, falls, and motor vehicle accidents. In this study, we examined the effects of the COVID-19 pandemic on pediatric nasal fractures. Methods Children with nasal fracture who applied to Malatya Training and Research Hospital during the year before the first case and the following year were included in this study. Data of 172 patients for the pre-pandemic period and 79 patients for pandemic were available and included in the study. Demographic information, clinical features, nasal fracture etiology, nasal fracture type, type and time of intervention, and other accompanying pathologies were recorded. Results While falls was the leading cause of fracture etiology before the pandemic (64 patients [37.21%]), assault seems to be the leading cause during the pandemic period (27 children [34.18%]). In the pre-pandemic period, the intervention for patients with nasal fractures was performed on an average of 5 days, while this period was calculated as an average of 6 days during the pandemic period. When the 2 groups are compared in terms of nasal fracture intervention time, it was seen that the intervention time was statistically significantly later in the pandemic period ( P < .001). According to the results of the analysis, the most cases in the pandemic period were seen in the fourth month, which indicated a-month period between 11 June and 11 July. Conclusions In conclusion, our number of nasal fracture cases was decreased during the pandemic period compared to the 1-year period before the pandemic. We observed the most common type IIA nasal fracture. We gave outpatient treatment to most of the patients. Our most common cause of fracture was assault. We intervened in our cases in an average of 6 days and preferred closed reduction most frequently. We could not find any study on the same subject in the literature, and we aimed to contribute to the literature with this study.

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

Blunt traumatic aortic injury: CT angiography images and short review of current literature

Blunt traumatic aortic injury in high-energy motor vehicle accidents is a rare but life-threatening condition. Proper treatment after prompt and accurate diagnosis is critical to reduce the death rate. The purpose of this article was to highlight the points to be considered in blunt traumatic aortic injury due to high-energy motor vehicle accident.