Incidence Of Accidents Research Articles

An Analysis of the Future Trends and Challenges of Autonomous Driving Technology

Abstract. Autonomous driving technology is one of the most groundbreaking innovations in the automotive industry in recent years. This article will explore the latest trends in autonomous driving, analyzing its historical development, current status, and future prospects, while also assessing its potential across different application scenarios. With the support of data and illustrations, this article aims to provide a comprehensive perspective to understand the development and challenges of this technology. As technology progresses, autonomous driving will significantly enhance traffic safety, efficiency, and convenience, ultimately leading the transportation industry into a new era. Although current autonomous driving systems still face challenges such as boundary identification and safety concerns, ongoing research and innovation across various fields will drive continuous improvements. In the future, autonomous driving is expected to become part of mainstream transportation, not only boosting traffic efficiency but also reducing the workload of drivers and lowering the incidence of traffic accidents. Plans for its widespread global adoption are already underway.

A study on Modeling and Simulation of Sports Training Injury Optimization from a Biomechanical Perspective

With the development of artificial intelligence technology today, sports training is one of the important ways to keep people healthy. Reasonable sports training can keep people happy, and can also improve the physical quality of athletes. However, because the intensity of sports training is greater than the physical endurance, it may lead to physical injury. The sports training injury model can collect the physical data of athletes, and simulate whether the actions that athletes want to practice are standard and correct through the collected data. If there are errors in the simulated exercise actions, the wrong exercise actions can be corrected in time, so as to reduce the incidence of sports injury accidents. The construction of the experimental model and the calculation and analysis process of the construction data are very complex, and the existing simulation models are difficult to achieve this, which has a significant impact on the subsequent development of the simulation model. In view of this phenomenon, this paper, on the basis of the sports training injury model, combined with the neural network method, conducted an effective research on the construction of the sports training injury model, and inspected the performance of the model by testing the accuracy of the evaluation of the injury risk level, the degree of sports training injury, and the accuracy of the evaluation of the prediction results of the sports training injury model. The experimental data shows that the maximum accuracy of the sports training injury model in the horizontal and vertical directions was 94.43% and 95.26% respectively, and the maximum accuracy of the single injury degree and the composite injury degree was 48.68% and 55.01% respectively, which had high accuracy and operational efficiency, and can effectively avoid the occurrence of training injury.

Enhancing intersection safety: Testing measures to reduce law violations in Loja (Ecuador)

This study evaluates the effectiveness of measures aimed at reducing traffic violations, specifically focusing on wrong-way driving, at intersections in Loja, Ecuador. The high incidence of accidents at these intersections, often resulting from wrong-way driving and non-compliance with traffic regulations, underscores the critical need for effective strategies to enhance road safety. To address this issue, we adopted a multidisciplinary approach to assess the impact of two specific interventions: the implementation of official warnings and the presence of traffic officers at a selected intersection. Data collection involved recording instances of traffic violations, administering road safety surveys, and monitoring the implementation of these interventions. The post-implementation analysis sought to determine the effect of these measures on driver behavior and overall traffic safety. Our findings indicate that while the interventions succeeded in increasing awareness about traffic violations, they did not produce a significant reduction in undesirable driving behaviors. This suggests that, although the presence of warnings and traffic officers is beneficial in raising awareness, these measures alone may not be sufficient to effect substantial behavioral changes. The research provides valuable insights for the development of more comprehensive road safety strategies and emphasizes the need for further studies to explore and address the underlying causes of traffic violations.

Your Body, My Business: Risk Governance in A Psychiatric Nursing Home.

A crucial long-term care resource for individuals with severe mental illness (SMI) in Taiwan is provided by psychiatric nursing homes. Given the higher incidence of physical illnesses and accidents among individuals with SMI, ensuring patient safety is an important aspect of quality care. However, there is limited literature exploring how the staff provides safe care for individuals with SMI. Investigates how the staff in a psychiatric nursing home managed residents' activities to mitigate risks and reduce physical injuries. Using a qualitative approach and drawing from Foucault's concept of 'disciplinary power', data were collected from field observations and staff interviews. Three themes emerged: (1) a gaze for efficiency, where checklists, timetables and spatial arrangements were used to efficiently manage residents' bodies; (2) controlling the deviant body, which entailed procedures to monitor and prevent risk behaviours and bodily signs through broad safety measures and (3) your body, my business, which described the reduced autonomy of residents over their behaviours as the responsibility largely shifted to the staff. The predominant risk management framework, driven by safety considerations, comprises residents' autonomy and undermines compassionate caring. Risk management should actively involve both staff and residents in decision-making.

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational modal decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.

Characterization of a large caliber explosively driven shock tube.

Research on evaluating weapon systems, building structures, and personnel protection has attracted considerable attention due to the high incidence of blast accidents. The explosively driven shock tube is an affordable and replicable method for investigating high pressure blast waves and extreme shock environments. A newly constructed large caliber explosively driven shock tube with an inner diameter of 2.5m and a length of 18m has been documented and characterized in this paper. It is capable of providing a peak pressure of at least 5.49 MPa in the test section with 160kg of TNT charges. The tube can produce an overpressure that is significantly higher than conventional shock tubes, which expands the capability to simulate a high overpressure blast load. A two-dimensional axisymmetric simulation model has been developed, validated, and calibrated for the characterization of the flow field inside the shock tube. The influence of the charge mass on the overpressure, arrival time, and positive impulse was discussed, and the planarity of the shock wave was also quantitatively characterized. To aid in designing further shock experiments and applications, a physics-based prediction model was developed using the dimensional analysis.

Development of an Optimized Accident Prediction Model for Construction Safety Using Big Data Analysis

Background and objective: The objective of this study was to develop an intuitive accident prediction model that can be readily applied at construction sites to effectively reduce the incidence of accidents through the analysis of construction accident data.Methods: These accidents significantly contribute to the construction industry's overall accident rate. Accident types were categorized into fatalities, injuries, and material damages to construct the accident prediction model. A total of 24 factors were considered across eight major variables, which were identified during the first and second phases of data preprocessing to analyze construction accident big data. Machine learning techniques were employed, specifically supervised learning and ensemble learning, to identify the optimal predictive model.Results: Among the models tested, XGBoost emerged as the most effective due to its highly balanced accuracy, even in the presence of class imbalance.Conclusion: The implementation of the XGBoost accident prediction model, along with the feature importance codes developed in this study, enables the prediction of accident types for specific tasks performed at construction sites. This predictive capability is expected to inform the implementation of targeted accident prevention measures, such as enhancing safety protocols or adjusting work procedures based on the prediction outcomes.

Predisposing and precipitating factors of post-operative delirium after transfemoral TAVI

Abstract Background Transcatheter aortic valve implantation (TAVI) represents the treatment of choice for aortic stenosis (AS) patients at higher age (> 75y) or with relevant comorbidities. Post-operative delirium (POD) is a frequent complication which has been associated with worse outcome. Thus, preventive options are highly needed. The aim of our single-center study was to investigate predisposing and precipitating factors of POD after transfemoral TAVI, with a focus on the influence of anaesthesia. Methods Risk factors for POD after TAVI were analysed in 2 merged cohorts of 480 study patients (43% women; mean age 80 years), enrolled in 2017-2022 (n=375, TAVI in 95% in general anaesthesia) and in 2022-2024 (n=105, TAVI in 91% in local anaesthesia). Incidence of POD was evaluated retrospectively by nursing documentation, medical records, and DRG coding. The association of demographics, comorbidities, echo parameters, procedural parameters, anaesthesia, and peri-procedural complications with POD incidence was investigated. Results In total, 29 patients (6%) developed POD, with a tendency towards a lower incidence in the later cohort (6.7% vs. 3.8%; p=0.28). Regarding baseline characteristics, patients experiencing POD were more likely to be male (p=0.03), frail (Katz Index <6, p=0.049), and to suffer from low-gradient AS (p=0.005 for paradoxical, p=0.02 for classical low-flow, low-gradient AS; see also fig. 1A). Total procedure time was significantly longer in patients with POD (median, 86 vs. 62 min, p=0.004; fig 1B), and a tendency towards a more frequent implantation of other than ballon-expandable valves could be observed (p=0.07). Concerning the role of anaesthesia, POD did not differ significantly after general anaesthesia (n=24/360, 6.7%), conscious sedation (n=1/19, 5.3%), and local anaesthesia (n=4/100, 4%) (p=0.38). Regarding procedural complications, POD patients were characterized by lower minimum hemoglobin post-TAVI (9.3 vs. 10.1 g/dl, p=0.05; fig. 1C) and higher incidences of intra-procedural resuscitation (11% vs. 2%, p=0.03), cerebrovascular accidents (10% vs. 2%, p=0.04) and new permanent pacemaker implantations (25% vs. 10%, p=0.02). In POD patients, long-term survival after TAVI was significantly reduced, with a median survival of only 2 years (Kaplan-Meier curves in fig. 2; p=0.001). Conclusions POD after TAVI stays common and unfavourable. The role of anaesthesia seems less pronounced than expected. Total procedure time and relevant anaemia after TAVI emerged as most important modifiable risk factors. Future research is warranted, since strategies to reduce precipitating factors for POD may have a significant impact on post-TAVI outcomes.Selected risk factors for PODConsequence of POD: Reduced Survival

Exploring the Impact of an Interactive Electronic Pegboard on Manual Dexterity and Cognitive Skills of Patients With Stroke: Preliminary Analysis.

As individuals age, the incidence and mortality rates of cerebrovascular accidents significantly rise, leading to fine motor impairments and cognitive deficits that impact daily life. In modern occupational therapy, assessing manual dexterity and cognitive functions typically involves observation of patients interacting with physical objects. However, this pen-and-paper method is not only time-consuming, relying heavily on therapist involvement, but also often inaccurate. Digital assessment methods, therefore, have the potential to increase the accuracy of diagnosis, as well as decrease the workload of health care professionals. This study examined the feasibility of an interactive electronic pegboard for the assessment and rehabilitation of patients with stroke. We explored the pegboard's clinical applicability by examining the relationship among stages, timing, and difficulty settings, as well as their alignment with patient capabilities. In total, 10 participants used a prototype of the pegboard for functional and task assessments; questionnaire interviews were conducted simultaneously to collect user feedback. Patients with stroke consistently required more time to complete tasks than expected, significantly deviating from the initial time frames. Additionally, the participants exhibited a slight reduction in performance levels in both manual dexterity and cognitive abilities. Insights from questionnaire responses revealed that the majority of participants found the prototype interface easy and enjoyable to use, with good functionality. This preliminary investigation supports the efficacy of interactive electronic pegboards for the rehabilitation of the hand functions of patients with stroke, as well as training their attentional and cognitive abilities. This digital technology could potentially alleviate the burden of health care workers, positioning it as a valuable and intelligent precision health care tool.

Research on Safety Evaluation of Stadium Reconstruction Construction Based on Combination Weighting Extension Model

As an important carrier of sports services and the main force participating in the “short board” project of urban development, the renovation and upgrading of old stadiums have become an important trend for the sustainable development of venues in the context of urban renewal, consumption upgrading, and national fitness. However, owing to the complexity of the transformation process, the probability of safety accidents continues to increase, posing a serious threat to national property security. In order to reasonably evaluate the safety of the stadium renovation construction process and reduce the incidence of accidents in the renovation project, this study proposed a safety evaluation model for the stadium renovation construction based on the combination weighting extension model. First, according to the 5M1E theory, 27 influencing factors were selected, a safety evaluation index system for stadium reconstruction construction was constructed, and the safety evaluation grade of the index was quantified. Second, based on the analytic hierarchy process (AHP) and improved entropy weight method, the combination weight of the index was determined, and a safety evaluation model was constructed using the matter–element extension theory. Finally, the established evaluation model was applied to the example of stadium renovation, and the construction safety level of the renovation project was obtained. The research results showed that the model has strong operability, and the evaluation results are reasonable and reliable, providing a new concept for the safety control of stadium reconstruction construction.

Impact of Applying PDCA Circulation Method on Surgical Care Interventions in the Operating Room on Patients Undergoing Cardiothoracic Surgery: A Retrospective Study

Background: The Plan-Do-Check-Act (PDCA) cycle is a widely recognized methodology for continuous quality improvement. This study evaluated the impact of applying the PDCA cycle on surgical care interventions in the operating room for patients undergoing cardiothoracic surgery. Methods: A retrospective study was conducted on the treatment of patients who underwent continuous cardiothoracic surgery in our Hospital from July 2020 to June 2023. Participants were divided into control and PDCA groups on the basis of the application of the PDCA cycle in their surgical care. Various outcomes were assessed, including psychological well-being (Hospital Anxiety and Depression Scale [HAMA] and Hamilton Depression Rating Scale [HAMD] scales), quality of life (using the QLQ-C30 tool), patient satisfaction, incidence of adverse accidents, clinical parameters, and laboratory measures. Results: The study comprised 133 patients, with 62 in the control group and 71 in the PDCA group. The psychological well-being, as measured by the HAMA and HAMD scales, of the PDCA group showed significant improvements compared with that of the control group (p < 0.05). The Quality-of-Life scores were higher in the PDCA group than in the control group (p < 0.05). In the PDCA group, the patient satisfaction scores related to hospital satisfaction were significantly higher than that in the control group, and the incidence of adverse accidents was lower (all p < 0.05). No significant differences were noted in clinical parameters (ejection fraction and B-type natriuretic peptide levels) nor laboratory measures (serum protein and C-reactive protein (CRP) levels) between the groups (p > 0.05 for all). Conclusion: The implementation of the PDCA cycle in the operating room for patients undergoing cardiothoracic surgery significantly improved psychological well-being and patient satisfaction and reduced the incidence of adverse accidents in the operating room, indicating that this quality improvement approach can enhance patient care experience. Although no significant differences were observed in clinical and laboratory parameters, the findings suggest that the PDCA cycle's strengths lie in its systematic approach to care process optimization and patient-centered outcomes.

The Correlation Between Adherence to Personal Protective Equipment and Incidents Involving Construction Workers at the Bali Hospital Project

Work accidents are unwanted events that harm people, cause damage to property, and work processes. In the construction sector, the work accident rate in Indonesia reaches 32%. This research aims to determine the relationship between compliance with the use of personal protective equipment (PPE) with the incidence of work accidents in construction sector workers at Bali International Hospital Sanur managed by PT PP (Persero) Tbk. This research used a quantitative method with a cross-sectional design. The research population was all construction workers in the Bali International Hospital Sanur project as many as 286 people, with random sampling techniques. The instrument used in data collection was a questionnaire, and data analysis was carried out univariate and bivariate using the Chi-square test. The results showed a relationship between the use of safety helmets and the incidence of occupational accidents in construction workers at Bali International Hospital Sanur by PT PP (Persero) Tbk (p = 0.11). The implication of this research is the importance of compliance with the use of PPE in preventing work accidents in the construction sector.

Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.

DPF-YOLOv8: Dual Path Feature Fusion Network for Traffic Sign Detection in Hazy Weather

Traffic sign detection plays an integral role in intelligent driving systems. It was found that in real driving scenarios, traffic signs were easily obscured by haze leading to traffic sign detection inaccuracy in assisted driving systems. Therefore, we designed a traffic sign detection model for hazy weather that can effectively help drivers to recognize road signs and reduce the incidence of traffic accidents. A high-precision traffic sign detection network has been designed to address the problem of decreased model recognition performance caused by external factors such as small size of traffic signs and haze obstruction in real-world scenarios. First, the default YOLOv8 was found to have low model detection accuracy in hazy weather occlusion conditions through experimental studies. Therefore, a deeper lightweight and efficient multi-branch CSP (Cross Stage Partial) module was introduced. Second, a dual path feature fusion network was designed to address the problem of insufficient feature fusion due to the small size of traffic signs. Finally, in order to be able to better simulate the real haze weather scene, we added fog to the raw data to enrich the data samples. This was verified through experiments on a public Chinese traffic sign detection dataset after fogging treatment, compared to the default YOLOv8 model. The improved DPF-YOLOv8 algorithm achieved 2.1% and 2.2% improvement in mAP@0.5 and mAP@0.5:0.95 performance metrics to 65.0% and 47.4%, respectively.

Optimized Right-Turn Pedestrian Collision Avoidance System Using Intersection LiDAR

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.

Efficient Deep Learning Methods for Detecting Road Accidents by Analyzing Traffic Accident Images

Speed is one of the major factors in car crashes. Many lives could have been saved if emergency services had been alerted to the disaster and arrived in time. For the sake of protecting valuable human lives, an effective automatic accident detection system with prompt reporting of the accident scene to emergency services is essential. Therefore, this research proposes some effective Deep Learning techniques that properly recognize the incidence of accidents. The paper introduces two different techniques for image classification, with a particular focus on distinguishing between accident and non-accident images. The dataset used for the proposed model is taken from Kaggle, which is a collection of CCTV images. The first approach is a hybridized TL-ML method that employs transfer learning techniques that use different pre-trained versions of convolutional neural networks to extract features from image datasets. These extracted features are then fed into various machine learning classifiers to categorize the images as either Accident or Non-accident. To make the final decision, a voting classifier is utilized to choose the best classification outcome from the set of previously employed machine learning classifiers. In the second method, a modified Convolutional Neural Network (CNN) called SpinalNet is adopted. The performance of these models was evaluated by comparing them with each other and with a customized CNN base model. SpinalNet consistently surpassed the other models in terms of Precision, Recall, F1-Score, and Accuracy, demonstrating its outstanding capabilities.

Major injuries and associated factors in traffic accidents among motorcycle food delivery riders during the COVID-19 pandemic in Thailand

Motorcycle food delivery riders (MFDRs) are at a higher risk of traffic accidents compared to regular motorcyclists. Their safety is a significant issue in many developing countries, particularly in Thailand, which has the highest rate of motorcyclist fatalities globally. This study aimed to determine the prevalence of traffic accidents and explore the association between risky riding behaviors, concerns for working conditions, and accidents among MFDRs. This cross-sectional study was conducted in Chiang Mai, Thailand. Data were collected from a total of 709 MFDRs through an online platform. Collected data included working conditions, risky riding behaviors, concerns about their work conditions, and experience with traffic accidents. In the past six months, the incidence of traffic accidents among MFDRs was 18.9 %., with 2.4 % experiencing hospitalized major injuries, 11.4 % having non-hospitalized major injuries, and 5.1 % having minor injuries. A total of 84.3 % of MFDRs exhibited at least one risky riding behavior, while 5.8 % engaged in ten or more such behaviors. Multivariable logistic regression analysis with adjustments for sex, age, and body mass index indicates that red-light running (adjusted odds ratio, aOR 2.78, 95 % CI 1.07 to 7.21) and being concerned for customer behaviors (aOR 2.66, 95 % CI 1.26 to 5.62) were significantly associated with major injuries. Additionally, factors such as sleeping less than 6 h per day (aOR 1.68, 95 % CI 1.10 to 2.56), riding the wrong way (aOR 2.49, 95 % CI 1.12 to 5.52), and having concern for customer behaviors (aOR 1.91, 95 % CI 1.03 to 3.55) were associated with total number of accidents. Red-light running and concerns for customer behavior in MFDRs are associated with a higher risk of traffic accidents with major injuries. Effective strategies and interventions should focus on strengthening the enforcement of traffic risk behaviors and encouraging food delivery companies to establish regulations relating to the workers’ concerns.

Stroke Prevention With Prophylactic Left Atrial Appendage Occlusion in Cardiac Surgery Patients Without Atrial Fibrillation: A Meta-Analysis of Randomized and Propensity-Score Studies.

The role of left atrial appendage occlusion (LAAO) in patients without previous atrial fibrillation (AF) is not established. This meta-analysis was conducted on patients with normal sinus rhythm who underwent cardiac surgery, with and without concomitant LAAO, to evaluate its effect on the incidence of cerebrovascular accidents (CVAs). A systematic review was conducted from inception until December 2023 for randomized and propensity-score studies comparing CVA in patients without AF undergoing cardiac surgery with or without LAAO. Six studies met our inclusion criteria with a total of 4130 patients: 2146 in the LAAO group and 1984 in the no-LAAO group. The risk ratio of postoperative AF was 1.05 (95% CI, 0.86-1.28); P=0.628. The CVA rates at 5 years were 6.8±1.0% in the no-LAAO group and 4.3±0.8% in the LAAO group (log-rank P=0.021). The Cox regression analysis for CVA in patients undergoing LAAO reported a hazard ratio of 0.65 (95% CI, 0.45-0.94); P=0.022. Landmark analysis at 4 years highlighted a significant difference in overall survival between no-LAAO and LAAO groups, 86±12.2% versus 89.6±11.0%; P=0.041. In this meta-analysis of patients without previous AF undergoing cardiac surgery, LAAO was associated with a decreased risk of CVA, no difference in the incidence of postoperative atrial fibrillation, and a significant overall survival benefit at a 4-year landmark analysis. Although these findings support LAAO, the randomized LeAAPS trial (Left Atrial Appendage Exclusion for Prophylactic Stroke Reduction Trial), LAA-CLOSURE trial (A Randomized Prospective Multicenter Trial for Stroke Prevention by Prophylactic Surgical Closure of the Left Atrial Appendage in Patients Undergoing Bioprosthetic Aortic Valve Surgery), and LAACS-2 trial (Left Atrial Appendage Closure by Surgery-2) will help define the effectiveness of LAAO in patients undergoing cardiac surgery who have risk factors for AF and CVA. URL: https://www.crd.york.ac.uk/prospero/; Unique identifier: CRD42024496366.

Evaluation of children applying to emergency service after motor vehicle trauma

BackgroundTraffic accidents are significant causes of death and serious injury in children. Children's clinical findings are more subtle and there is a risk of faster deterioration. On the other hand, radiation sensitivity due to imaging is also high. These challenging factors highlight the importance of approaching pediatric trauma cases. In our study, we aimed to contribute to current approaches by examining the clinical course and profiles of children involved in traffic accidents. This study aimed to analyze the profiles and clinical courses of children involved in road traffic accidents. MethodsPediatric patients brought to our hospital due injuries from traffic accidents were retrospectively investigated. ResultsDuring the two-year period analyzed in the study, 605 child patients applied due to traffic accidents, accounting for 5.4 % of pediatric trauma cases. The majority of the children were between the ages of 12–17, with most accidents occurring in the summer. In total, 56.9 % were from in-vehicle traffic accidents, 36.7 % were from extra-vehicular traffic accidents, and 6.4 % were from motorcycle accidents. The imaging rate among the patients was 89 %, and 50.6 % had more than one tomography. More than half of the cases had no pathological findings, and only 6 % required hospitalization. Most cases were treated successfully with medical care, while 21.4 % needed interventional procedures, and 10 % received no treatment. The most common injuries were superficial skin lesions (11.1 %) and extremity fractures (10.2 %), with orthopedics being the most consulted department. Fractures were most common in the tibia and were more likely in motorcycle accidents than in-car accidents. ConclusionIn-vehicle accidents were the most frequent type of accident, and children under 4 years old had a higher incidence of in-car accidents than other age groups. Males made up 60 % of the cases, with boys having higher rates of motorcycle and extra-vehicular accidents compared to girls. While most children had no injuries from the accidents, those who did generally recovered well with outpatient monitoring. On the other hand, 89 % of patients underwent imaging at a high rate.

Descriptive analysis of road traffic accidents in children aged 0-2 years

Aims: This study aims to investigate road traffic accidents (RTAs) involving infants and toddlers, defined as children from birth to 2 years old, focusing specifically on their epidemiology and outcomes. Methods: A retrospective, single-center, observational, and cross-sectional study was conducted in the Emergency Department of a university-affiliated training and research hospital. Data were collected from July 1, 2019, to January 1, 2024. Results: During the study period, 659 cases involved pediatric RTAs patients (individuals under 17 years old), with 41 cases specifically concerning infants and toddlers. These patients had a mean age of 12.98 ± 5.64 months, and 27 (65.9%) were boys. The median Pediatric Trauma Score for was 9 (range: 5-12) while the median pGCS score was 15 (range: 14-15). Motor vehicle accidents were the leading cause of injuries, accounting for 92.6% of cases, followed by motorcycle accidents (4.8%) and pedestrian-vehicle collisions (2.6%). Hospitalized patients exhibited various injuries, including femur fractures, subdural hematoma, orbital roof fracture, scalp hematomas, and lung contusion. Surgical intervention was required in only one case for a femur shaft fracture. Fortunately, no in-hospital deaths occurred among the study participants. Conclusion: This study underscores the importance of adult supervision to prevent RTAs and emphasizes the critical need for strict enforcement of child passenger safety laws to reduce the incidence of motor vehicle accidents involving infants and toddlers.