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Background: Machine perfusion (MP) offers extended preservation of vascularized complex allografts (VCA), but the diagnostic value of histology using hematoxylin and eosin (H&E) in detecting ischemia-reperfusion injury (IRI) in muscle cells remains unclear. This study aims to document the application of the Histology Injury Severity Score (HISS) and to assess whether additional staining for nicotinamide adenine dinucleotide (NADH) and membrane attack complex (MAC) improves IRI detection in a porcine limb replantation model. Methods: The forelimbs of 16 Dutch Landrace pigs were amputated and preserved for 24 h using hypothermic MP (n = 8) with Histidine-Tryptophan-Ketoglutarate (HTK) or for 4 h with SCS (n = 8) before heterotopic replantation and 7 days of follow-up. Muscle damage was assessed via biochemical markers and light microscopy using H&E, NADH, and MAC at baseline, post-intervention, and post-operative day (POD) 1, 3, and 7 timepoints, using the HISS and a self-developed NADH and MAC score. Results: H&E effectively identified damaged muscle fibers and contributed to IRI assessment in porcine limbs (p < 0.05). The highest HISS was measured on POD 3 between MP (4.9) and SCS (3.5) (p = 0.029). NADH scores of both preservation groups varied over the 7-day follow-up and were statistically insignificant compared with baseline measurements (p > 0.05). MAC revealed no to minimal necrotic tissue across the different timepoints. Conclusions: This study documents the application of the HISS with H&E to detect IRI in muscle fibers. NADH and MAC showed no significant added diagnostic utility. The 24 h MP showed similar muscle alterations using the HISS compared to that of the 4 h SCS after a 7-day follow up.

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Crash type, a key contributory factor of crash injury severity level, is typically included in crash severity models as an explanatory variable. However, certain unobserved factors could influence both the crash type and crash injury severity simultaneously. As such, there could exist an endogenous effect of crash type on crash injury severity. The present paper investigates this hypothesis using data from highway ramp areas. These locations tend to be crash-prone because of the frequent lane changes and speed differentials associated with merging, diverging, and weaving of vehicles at those locations. Conventional approaches used in past ramp safety studies modeled crash type and crash injury severity separately, not addressing the endogenous effect of crash type on crash severity at these locations. In this study, a random parameter recursive bivariate probit model is proposed to model the crash type (hit-object and rollover) and crash injury severity at ramp areas simultaneously and to account for any endogenous effect of crash type. The study used highway crash data from ramp areas at highway located in North Carolina from 2016 to 2018. The results indicate that the proposed model can and does capture the endogenous effect of crash type. The likelihood of injury for a rollover crash would be underestimated if endogeneity were not considered. Other exogenous variables including aberrant driving behavior, safety belt, road surface condition, lighting condition, area type, crash location, and ramp type that affect the type and injury severity of crashes at highway ramp areas were identified. The exogenous variables that are significant only for the crash type, such as vehicle type, and speed limit, were detected to have indirect effects on the crash injury severity. Furthermore, the effects of individual heterogeneity of the explanatory variables are considered. Female drivers and old drivers are statistically significant in the means of random parameters. The findings shed light on the potential need and effectiveness of prospective traffic management and control measures to mitigate crash risk at highway ramp areas.

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The effect of vehicle transmission type on driver injury severities have not been thoroughly studied. The study used four-year historical crash data that occurred between the year 2019 and 2022 in Ghana. The data shows 1856 and 2272 crashes for automatic and manual transmission, respectively. The study examined the factors influencing driver injury severity in crashes involving vehicles with manual and automatic transmissions, using Random Parameter Mixed Logit Model to account for heterogeneity in the dataset. It was observed that use of manual transmission is related to a higher risk of incapacitating and fatal injuries compared to automatic transmission. Specifically, for automatic transmission vehicle-involved crashes, factors related to fatal injury were overaged vehicles, public transport, morning and evening peak hours, head-on and rollover crashes. Crashes involving saloon cars and low age cars were associated with incapacitating injury whiles rainy weather condition was related to both fatal and incapacitant injuries. Regarding manual transmission, fatal injury was associated with crashes involving male and novice drivers, cars, pickup trucks, HGV, public transports, morning and evening peak hours, rainy weather conditions and curved roads. Also, buses, private cars and trip distance were related to incapacitating injury. The rollover crashes and overaged vehicles were also associated with both fatal and incapacitating injuries. Four random parameters demonstrated heterogeneity in means, with two factors influencing the variances of two parameters for automatic transmission model. For the manual transmission model, five random parameters showed heterogeneity in means, with four variables influencing the variances of three parameters. These findings are valuable for policymakers, manufacturers, and drivers in implementing targeted interventions and safety measures to promote road safety.

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OBJECTIVE: Predicting high-value care outcomes is crucial in managing pediatric traumatic brain injuries (TBIs), where timely and accurate prognostication can significantly influence treatment decisions and resource allocation. This study aimed to enhance understanding of how well scoring systems such as the Trauma and Injury Severity Score (TRISS) can forecast high-value care outcomes. Furthermore, the authors compared the predictive power of TRISS with the routinely used Injury Severity Score (ISS). METHODS: The authors performed a retrospective review of their institutional database from June 2016 to June 2023 to identify cases of TBI based on a modified Centers for Disease Control and Prevention framework. Prolonged length of stay (LOS) was defined as a hospital stay falling into the upper quartile of the overall cohort. Discharge to an inpatient rehabilitation facility, acute care hospital, or foster care or death was defined as a nonroutine discharge disposition. Emergency department (ED) transfer to the intensive care unit (ICU) or operating room (OR) was defined as a proxy for severity of injuries. Multivariate logistic regression models were used to explore the association between ISS, TRISS, and high-value care outcomes. The DeLong test was used to assess the differences between the areas under the receiver operating characteristic curve (AUROCs). RESULTS: This study included 2705 patients with a mean age ± SD of 7.28 ± 5.46 years (63% male). In the overall cohort, 28% experienced prolonged LOS, 7% had a nonroutine discharge disposition from the hospital, and 23% were transferred to the ICU/OR from the ED. In multivariate regression models, both TRISS and ISS were correlated with higher odds of prolonged LOS, nonroutine discharge disposition, and transfer to the ICU/OR from the ED (all p < 0.001). TRISS had a significantly greater AUROC than ISS for nonroutine discharge disposition (0.883 vs 0.849, p < 0.001) and transfer to the ICU/OR (0.898 vs 0.887, p = 0.045), but this result was not significant for prolonged LOS (0.873 vs 0.880, p = 0.140). CONCLUSIONS: TRISS and ISS are effective tools for predicting high-value care outcomes in pediatric TBI. Utilizing these resources can assist healthcare providers in making informed, risk-adjusted predictions.

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Despite significant progress in the medical field, there is still a pressing need for minimal-invasive tools to assist with decision-making, especially in cases of polytrauma. Our team explored the potential of serum-derived large extracellular vesicles, so called microparticles/microvesicles/ectosomes, to serve as a supportive tool in decision-making in polytrauma situations. We focused on whether monocyte derived large EVs may differentiate between polytrauma patients with internal organ injury (ISS > 15) and those without. Thus, we compared our EV data to soluble biomarkers such as tumour necrosis factor alpha (TNF alpha) and Interleukin-8 (IL-8). From the blood of 25 healthy and 26 patients with polytrauma large EVs were isolated, purified, and characterized. TNF alpha and IL-8 levels were quantified. We found that levels of these monocyte derived large EVs were significantly higher in polytrauma patients with internal organ damage and correlated with the ISS. Interestingly, we also observed a decline in AnnV+CD14+ large EVs during normal recovery after trauma. Thus, inflammatory serological markers as TNF alpha and as IL-8 demonstrated an inability to discriminate between polytrauma patients with or without internal organ damage, such as spleen, kidney, or liver lacerations/ruptures. However, TNF and IL-8 levels were elevated in polytrauma cases overall when contrasted with healthy non-traumatic controls. These findings suggest that delving deeper into the potential of AnnV+ large EVs derived from monocytes could highly beneficial in the managment of polytrauma, potentially surpassing the efficacy of commonly used serum markers.

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Introduction: The global estimate reflects 4.4 million injury-related deaths every year and the thoracoabdominal region is frequently injured since it is a wide area. Methods: An autopsy-based cross-sectional study was conducted using a convenient sampling technique for three years. A Chi-square test was conducted for categorical variables. A P value of less than 0.01 was considered statistically significant. Results: Out of 80 cadavers, the male:female ratio was 1:0.31. The commonest age group was 20-29 years. Victims of road traffic accidents were 54 (67.5%) followed by falls from height 10 (12.5%). External thoracic injuries were commoner than internal and vice versa in abdominal injuries. Liver injuries were major in number. Combined thoracic-abdominal and associated injuries were observed in 67 (83.75%) victims. The commonest cause of death was craniocerebral injury combined with hemorrhagic shock (36.25%). Eighteen (22.5%) victims died on the spot having an Injury Severity Score (ISS) of 73.37 followed by a survival time of 1-7 days in 17 (21.25%) cases. A significant correlation was found between ISS and survival period. Conclusion: All thoracic and abdominal injuries represent a possible increase in morbidity and mortality; hence, working toward their prevention and timely intervention is necessary.

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Soluble CD163 (sCD163) is a biomarker of macrophage activation, not previously investigated in the circulation of traumatized patients. A biobank of 398 adult trauma patients was analyzed. Patients with an Injury Severity Score (ISS) >8 served as trauma patients (n = 195) and those with ISS ≤8 as trauma controls (n = 203). Serum samples obtained upon admission, 15h and 72h after were analyzed for sCD163 using an in-house ELISA. Multiple linear regression was used to analyze the association between admission levels of sCD163 with, 1: overall trauma severity (ISS), and 2: severity of injury to specified organs using Abbreviated Injury Score (AIS) and Glasgow Coma Scale (GCS). The association between the peak level of sCD163 with 1-year all-cause mortality was analyzed by logistic regression analysis. Median admission levels of sCD163 were higher in trauma patients than trauma controls [2.32 (IQR 1.73 to 2.86) vs. 1.92 (IQR 1.41 to 2.51) mg/L, p < 0.01]. Worsening GCS score was associated with a 10.3% (95% CI: 17.0 to 3.1, p < 0.01) increase in sCD163. Increasing Head-AIS score was associated with a 5.1% (95% CI: -0.5 to 11.0, p = 0.07) increase in sCD163. The remaining AIS scores and ISS were not consistently associated with sCD163 admission levels. Each mg/L increase in sCD163 peak level had an odds ratio 1.34 (95%CI: 0.98 to 1.83), p = 0.06) after adjustment for age, sex, and GCS. Circulating sCD163 is increased in traumatized patients and associated with worsening GCS. Our findings suggest an association between circulating sCD163 levels with 1-year all-cause mortality.

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BACKGROUND AND OBJECTIVES: This study aimed to assess the impact and predicted outcomes of patients with multiple trauma by identifying the prevalence of trauma sustained and associated complications. MATERIALS AND METHODS: This retrospective cohort study focused on individual characteristics of patients with multiple trauma admitted to our County Emergency Hospital. The final table centralized the characteristics of 352 subjects aged between 3 and 93 years who presented with multiple trauma from 2015 to 2021. Inclusion criteria for this study were the presence of multiple trauma, intervention times, mentioned subjects' ages, and types of multiple trauma. RESULTS: Patients with multiple trauma face an increased risk of mortality due to the underlying pathophysiological response. Factors that can influence the outcomes of multiple-trauma patients include the severity of the initial injury, the number of injuries sustained, and the location of injuries. CONCLUSION: The first 60 min after trauma, known as the "golden hour," is crucial in determining patient outcomes. Injuries to the head, neck, and spine are particularly serious and can result in life-threatening complications.

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The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

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Interleukin-6 (IL-6) was suggested as a potential target for intervention to mitigate brain injury. However, its neuro-protective effect in post-resuscitation care has not been proven. We investigated the time-course of changes in IL-6 and its association with other markers (systemic inflammation and myocardial and neuronal injury), according to the injury severity of the cardiac arrest. This retrospective study analyzed IL-6 and other markers at baseline and 24, 48, and 72 h after the return of spontaneous circulation. The primary outcome was the association of IL-6 with injury severity as assessed using the revised Post-Cardiac Arrest Syndrome for Therapeutic Hypothermia scoring system (low, moderate, and high severity). Of 111 patients, 22 (19.8%), 61 (55.0%), and 28 (25.2%) had low-, moderate-, and high-severity scores, respectively. IL-6 levels were significantly lower in the low-severity group than in the moderate- and high-severity groups at baseline and at 24 h and 72 h (p < 0.005). While IL-6 was not independently associated with neuronal injury markers in the low-severity group, it was demonstrated to be associated with it in the moderate-severity (ß [95% CI] = 4.3 [0.1-8.6], R2 = 0.11) and high-severity (ß [95% CI] = 7.9 [3.4-12.5], R2 = 0.14) groups. IL-6 exhibits distinct patterns across severity and shows differential associations with systemic inflammation or neuronal injury.

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Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both uncorrelated and correlated joint random-parameters bivariate probit (RPBP) approaches (statistical methods) and a cross-stitch multilayer perceptron (CS-MLP) network (a data-driven method), were estimated and compared against three separate models: Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), and MLP networks (all data-driven methods). Data on 15,980 two-vehicle RE crashes were collected over a two-year period, from January 1, 2021, to December 31, 2022, considering two possible levels of injury severity: no injury and injury/fatality for both drivers of following and leading vehicles. The comparative performance analysis demonstrates the superior predictive capability of the CS-MLP network over the uncorrelated/correlated joint RPBP model, SVM, XGBoost, and MLP networks in terms of recall, F-1 Score, and AUC. Significantly, numerous shared variables influence the injury severity outcomes for the following and leading vehicles across both statistical and data-driven approaches. Among these factors, the following vehicle (a truck) and the leading vehicle (a passenger car) demonstrate contrasting effects on the injury severity outcomes for both vehicles. Furthermore, the SHapley Additive exPlanations (SHAP) values from the CS-MLP network visually show the relationship between Δν and injury severity, revealing non-linear trends unlike the average effects shown by statistical methods. They indicate that the least injury outcomes for both following and leading vehicles occurs at a Δν of 0 to 10 mph, matching observed patterns in RE crash data. Additionally, a marked variation in the trend of SHAP values for the two vehicles is noted as the speed difference increases. Therefore, the findings affirm the superior performance of joint model development and substantiate the non-linear impacts of speed difference on injury outcomes. The adoption of dynamic speed control measures is recommended to mitigate the injury outcomes involved in two-vehicle RE crashes.

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Using data from a developing country, the current study develops a copula-based joint modeling framework to study crash type and driver injury severity as two dimensions of the severity process. To be specific, a copula-based multinomial logit model (for crash type) and generalized ordered logit model (for driver severity) is estimated in the study. The data for our analysis is drawn from Bangladesh for the years of 2000 to 2015. Given the presence of multiple years of data, we develop a novel spline variable generation approach that facilitates easy testing of variation in parameters across time in crash type and severity components. A comprehensive set of independent variables including driver and vehicle characteristics, roadway attributes, environmental and weather information, and temporal factors are considered for the analysis. The model results identify several important variables (such as driving under the influence of drug and alcohol, speeding, vehicle type, maneuvering, vehicle fitness, location type, road class, road geometry, facility type, surface quality, time of the day, season, and light conditions) affecting crash type and severity while also highlighting the presence of temporal instability for a subset of parameters. The superior model performance was further highlighted by testing its performance using a holdout sample. Further, an elasticity exercise illustrates the influence of the exogenous variables on crash type and injury severity dimensions. The study findings can assist policy makers in adopting appropriate strategies to make roads safer in developing countries.

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Understanding injury severity is essential to inform injury prevention practice. The aims of this scoping review were to investigate how running-related injury (RRI) severity is measured, compare how it differs across studies, and examine whether it influences study outcomes (i.e., injury rates and risk factor identification). This scoping review was prospectively registered with Open Science Framework. A systematic electronic search was conducted using PubMed, Scopus, SPORTDiscuss, MEDLINE, and Web of Science databases. Included studies were published in English between January 1980 and December 2023, investigated RRIs in adult running populations, and included a measure of injury severity. Results were extracted and collated. Sixty-six studies were included. Two predominant primary criteria are used to define injury severity: the extent of the effect on running and/or the extent of the physical description. When secondary definition criteria are considered, 13 variations of injury severity measurement are used. Two approaches are used to grade injury severity: a categorization approach or a continuous numerical scale. Overall, the measurement of RRI severity is relatively inconsistent across studies. Less than half of studies report incidence rates per level of injury severity, while none report specific risk factors across levels, making it difficult to determine if the approach to measuring injury severity influences these study outcomes. This lack of information is possibly contributing to inconsistent rates of RRIs reported, and the lack of clarity on risk factors.

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BACKGROUND: Human-elephant conflicts (HECs) are becoming a disturbing public health concern in eastern India. This study highlights the pattern of injuries, epidemiological factors, and outcomes among the victims who survived an elephant attack (EA). METHODS: This retrospective observational study was conducted in a tertiary care hospital. Data were retrieved from the medical records of EA victims who presented to the emergency department of the hospital over five years (January 2019-January 2024). Data regarding sociodemographic characteristics, injury mode, injury pattern, radiological findings, emergency procedures, and outcome variables (admission, length of intensive care unit and hospital stay, and death) were collected. RESULTS: In total, 45 EA victims were included in this study. The mean participant age was 45.8 ± 14.57 years. Of the total participants, 35 (78 %) were men. Most EAs [n = 18 (40 %)] occurred in the forest area and during the early morning hours between 4am and 8am [n = 18 (40 %)] of the winter season [n = 37 (82 %)] and were unprovoked [38 (84 %)]. Of the total injuries, 26 (67 %) injuries were due to the direct mode of EA and 13 (33 %) were due to the indirect mode. The most common mechanism of EA was using the trunk and foot [20 (51 %)], followed by the tusk [6 (15 %)]. The median ISS in victims was 20 (13-29). The median AIS score of chest injuries was 1 (0-3). Thirteen (29 %) patients were positive on e-FAST. Of the total EA victims, 12 (26 %) were admitted to the intensive care unit (ICU) and 17 were admitted to the wards. Severe chest injury (AIS score ≥ 3) (p = 0.003), direct mode of injury, and polytrauma (ISS > 16) were identified as significant factors contributing to ICU admission. The median ICU stay of the victims was 6 (3-8) days, and the median length of hospital stay was 7 (0.5-11) days. One inpatient mortality was noted. CONCLUSION: Middle-aged men were the most common victims of EA occurring during the early morning hours. Extremity and soft tissue injuries were most common, followed by chest and abdominal injuries. Severe chest injury resulted in ICU admission and extended hospitalization.

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BACKGROUND: There is extensive literature describing the application of telemedicine techniques to trauma care. However, there is a paucity of articles showing quantitative assessment of its safety and efficacy. This structured review examines articles with quantitative assessment of telemedicine's impact in acute trauma care. METHODS: Medline and CINAHL databases were searched for peer-reviewed articles that quantitatively assess the impact of telemedicine on diagnostic accuracy, clinical decision-making, emergency department length of stay, transfer rates, and mortality in initial trauma management. RESULTS: Only 9 of the 408 screened articles met the criteria for quantitative assessment. Telemedicine appears to be preferentially used for more severely injured patients. Limited quality evidence supports procedural interventions at remote sites. Telemedicine may help abbreviate pre-transfer length of stay. However, its impact on diagnosis and mortality remains unclear. CONCLUSIONS: Telemedicine's potential to enhance the quality and efficiency of trauma care, especially for resource-scarce areas, warrants continued quantitative research.

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This study investigates the impacts of various factors on e-bike riders' injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders' illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers' speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders' injury. Moreover, both drivers and riders' illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles' involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles.

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This study investigated the impact of the Injury Severity Score (ISS) on treatment approaches and survival outcomes in trauma patients, focusing on comparing elderly (≥65 years) with non-elderly patients. It analyzed adult trauma cases with abnormal Revised Trauma Scores from January to December 2019, categorizing patients into three severity groups based on ISS: mild (1-8), moderate (9-15), and severe (≥16). The study examined how ISS influenced therapeutic interventions and survival among elderly patients, comparing these outcomes to non-elderly patients using multivariable logistic regression analysis. In 16,336 adult trauma cases out of 52,262 patients, including 4886 elderly and 11,450 non-elderly patients, findings revealed that in the severe group, elderly patients had a lower, though not statistically significant, incidence of surgical or embolization interventions compared to the moderate group, differing from non-elderly patients. No significant differences were observed in the mild group between elderly and non-elderly patients. However, elderly patients had higher intervention rates in the moderate group and lower in the severe group, with significantly lower survival-to-discharge rates in the severe group. The ISS is insufficient for assessing trauma severity in elderly patients. Additional tools are needed for better evaluation and treatment decisions.

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BACKGROUND: Penile trauma is associated with debilitating complications. Apart from its sparse literature, contextually relevant evidence on aetiology and standardized severity grading, and its stratification by legal age are needed. This study aimed to describe the frequency, aetiology and injury severity grade of patients, and explore the association of specific factors by legal age. SUBJECTS, MATERIALS AND METHODS: A single-centre retrospective cross-sectional study was conducted in our centre. Male patients with penile trauma managed by urologists were included. Data on age, year of presentation, aetiology, penile injury extent and co-existing genitourethral injuries were obtained. Descriptive and inferential statistics were undertaken using SPSS and MedCalc. Pvalue < 0.05 was considered significant. RESULTS: Forty-two patient records were analyzed. The median age and interquartile range for legal minors and legal adults were 9.5 (5.0-14.8) years and 31.0 (22.5-41.0) years, and they constituted 28.6% (n=12) and 71.4% (n=30) of the study population respectively. Penile trauma was isolated in 26.2% (n=11) of the total population.Its annual frequency was below the median volume between 2001 and 2008 but shifted to a rate above the average level between 2017 and 2021. The rate of conveyor belt-related aetiology and high-grade trauma was 41.7% and 75.0% in legal minors while it was 26.1% and 53.3% respectively in legal adults. CONCLUSIONS: Grade IV trauma was the most predominant while conveyor belt injury was the most common cause of the injury. Albeit limitations of statistical comparison, the rate of conveyor belt injury and high-grade trauma was numerically high in legal minors.

CONTEXTE: Les traumatismes du pénis sont associés à des complications débilitantes. Outre la rareté de la littérature, des preuves contextuel l ement per tinentes sur l ' étiol ogie et l a classification standardisée de la gravité, ainsi que leur stratification par âge légal, sont nécessaires. Cette étude visait à décrire la fréquence, l'étiologie et le degré de gravité des blessures des patients, et à explorer l'association de facteurs spécifiques par âge légal. SUJETS, MATÉRIELS ET MÉTHODES: Une étude rétrospective transversale monocentrique a été réalisée dans notre centre. Les patients masculins ayant subi un traumatisme pénien pris en charge par des urologues ont été inclus. Les données sur l'âge, l'année de présentation, l'étiologie, l'étendue de la blessure pénienne et les blessures génito-urétrales coexistantes ont été obtenues. Des statistiques descriptives et inférentielles ont été réalisées à l'aide de SPSS et MedCalc. Une valeur de p < 0,05 a été considérée comme significative. RÉSULTATS: Quarante-deux dossiers de patients ont été analysés. L'âge médian et l'intervalle interquartile pour les mineurs légaux et les adultes légaux étaient de 9,5 (5,0-14,8) ans et 31,0 (22,5-41,0) ans, représentant respectivement 28,6 % (n=12) et 71,4 % (n=30) de la population étudiée. Le traumatisme pénien était isolé chez 26,2 % (n=11) de la population totale. Sa fréquence annuelle était inférieure au volume médian entre 2001 et 2008, mais a dépassé le niveau moyen entre 2017 et 2021. Le taux d'étiologie liée aux tapis roulants et de traumatismes graves était de 41,7 % et 75,0 % chez les mineurs légaux, contre 26,1 % et 53,3 % respectivement chez les adultes légaux. CONCLUSIONS: Le traumatisme de grade IV était le plus prédominant, tandis que les blessures causées par les tapis roulants étaient la cause la plus courante de lésion. Bien que limitées par des comparaisons statistiques, les taux de blessures par tapis roulant et de traumatismes graves étaient numériquement élevés chez les mineurs légaux. MOTS-CLÉS: Facteurs d'âge, Étiologie, Score de gravité des blessures, Pénis.

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Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is essential for implementing countermeasures aimed at preventing severe injury incidents and achieving Vision Zero goals. With the wealth of traffic crash data collected by various agencies, researchers have a valuable opportunity to conduct data-driven studies and employ various modeling methods to gain insights into the correlated factors affecting injury severity in traffic crashes. Machine learning models, owing to their superior predictive power compared to statistical models, are increasingly being adopted by researchers. These models, in conjunction with interpretation techniques, can reveal potential relationships between crash injury severity and contributing factors. Traffic crashes are inherently tied to geographic locations, distributed across road networks influenced by diverse socioeconomic and geographical factors. Recognizing spatial heterogeneity in traffic safety is crucial for tailored safety measures to address speeding-related crashes, as a one-size-fits-all approach may not work effectively everywhere. However, most existing machine learning models are unable to incorporate the spatial dependency among observations, such as traffic crashes, which hinders their ability to uncover spatial heterogeneity in traffic safety. To address this gap, this study introduces the Geographically Weighted Neural Network (GWNN) model, a spatial machine-learning model that integrates neural network (NN) and geographically weighted modeling approaches to investigate spatial heterogeneity in speeding-related crashes. Unlike the traditional NN model, which trains a single set of model parameters for all observations, the GWNN trains a local NN model for each crash location using a spatially weighted subsample of nearby crashes, allowing for the quantification of corresponding local effects of features through calculating local marginal effects. To understand the spatial heterogeneity in speeding-related crashes, this study extracted two years (2020 and 2021) of speeding-related crash data from Alabama for the development of the GWNN local models. The modeling results show significant spatial variability among several factors contributing to injury severity in speeding-related crashes. These factors include driver condition, vehicle type, crash type, speed limit, weather, crash time and location, roadway alignment, and traffic volume. Based on the GWNN modeling results, this study identified three types of spatial variations in relationships between contributing factors and crash injury severity: consistent positive associations, consistent negative associations, and inverse associations (i.e., marginal effects can vary between positive and negative depending on the location). This study contributes by integrating advanced machine learning and spatial modeling approaches to uncover intricate spatial patterns and factors influencing injury severity in speeding-related crashes, thereby facilitating the development of targeted policy implementations and safety interventions.

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In this single-center, retrospective, observational study, we aimed to assess the severity at which patients with trauma tend to develop metabolic disturbances that worsen their Controlling Nutritional Status (CONUT) scores. Participants were general adult patients with trauma hospitalized for at least one week. Injury Severity Scores (ISSs) at admission and CONUT scores one week later were calculated, and correlation coefficients were examined. The receiver operating characteristic (ROC) curve was used to calculate the ISS cutoff value for a CONUT score of 5 or more on day 7 of hospitalization. The ISS was assessed using multiple logistic regression analysis to determine whether it predicts worse nutritional status. Forty-nine patients were included. ISSs correlated with CONUT scores on day 7 (r = 0.373, p = 0.008). Using the ROC curve, the cutoff value for the ISS was 23.5. Multiple logistic regression analyses showed that a high ISS (odds ratio [OR], 1.158; 95% confidence interval [CI], 1.034-1.296; p = 0.011) and older age (OR, 1.094; 95% CI, 1.027-1.165; p = 0.005) were associated with a CONUT score 5 or more on day 7 of hospitalization. Patients with trauma with an ISS of 24 or higher have worsening CONUT scores during hospitalization; these patients require careful nutritional management.