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The mechanism of injury (MOI) criteria assist in determining which patients are at high risk of severe injury and would benefit from direct transport to a trauma center. The goal of this study was to determine whether the prognostic performance of the Centers for Disease Control's (CDC) MOI criteria for motor vehicle collisions (MVCs) has changed during the decade since the guidelines were approved. Secondary objectives were to evaluate the performance of these criteria for different age groups and evaluate potential criteria that are not currently in the guidelines.Data were obtained from NASS and Crash Investigation Sampling System (CISS) for 2000-2009 and 2010-2019. Cases missing injury severity were excluded, and all other missing data were imputed. The outcome of interest was Injury Severity Score (ISS) ≥16. The area under the receiver operator characteristic (AUROC) and 95% confidence intervals (CIs) were obtained from 1,000 bootstrapped samples using national case weights. The AUROC for the existing CDC MOI criteria were compared between the 2 decades. The performance of the criteria was also assessed for different age groups based on accuracy, sensitivity, and specificity. Potential new criteria were then evaluated when added to the current CDC MOI criteria.There were 150,683 (weighted 73,423,189) cases identified for analysis. There was a small but statistically significant improvement in the AUROC of the MOI criteria in the later decade (2010-2019; AUROC = 0.77, 95% CI [0.76-0.78]) compared to the earlier decade (2000-2009; AUROC = 0.75, 95% CI [0.74-0.76]). The accuracy and specificity did not vary with age, but the sensitivity dropped significantly for older adults (0-18 years: 0.62, 19-54 years: 0.59, ≥55 years: 0.37, and ≥65 years: 0.36). The addition ofThe MOI criteria for MVCs in the current CDC guidelines still perform well even as vehicle design has changed. However, the sensitivity of these criteria for older adults is much lower than for younger occupants. The addition of

The Abbreviated Injury Scale (AIS) is an anatomic-based injury coding system that strives to provide sufficient detail to differentiate unique injuries for the purposes of research and quality assurance, while limiting the total number of codes to facilitate efficient use. It has been shown that a substantial portion of codes are unused in automotive-trauma specific databases. The goal of this study was to determine the percentage of codes utilized in a nationwide trauma registry that includes multiple mechanisms of injury. Secondary objectives were to examine unused codes and determine the number of codes that were most frequently utilized. Data were obtained from the National Trauma Data Bank (NTDB) years 2016 and 2017. All injury data were recorded using AIS version 2005 update 2008 (AIS08), which contains 1,999 distinct injury codes. The percentage of the total number of AIS08 codes used in NTDB were determined for each year individually and the combination of both years. The unused codes were then examined manually to identify common characteristics. Finally, the number of codes that provided 95% coverage of all recorded injuries was calculated. There were 6,661,110 injuries recorded for 1,953,775 patients in NTDB over the two-year period. A small percentage of codes had an incorrect severity level (0.07%) or an incorrect injury code (0.0002%). There were 1,987 (99.4% of the entire AIS dictionary) unique AIS08 codes utilized in each year, with the unused codes varying between years. The unused codes tended to involve specific nerves, dural sinuses, or severe, bilateral injuries. During the combined two-year period, 1,996 (99.8% of the entire dictionary) unique AIS08 codes were used. Although almost every code was used at least once, 95% of the injuries in NTDB used only the 631 (31.6%) most frequent AIS08 codes. In contrast to automotive specific databases, nearly all the AIS08 codes are used each year in the NTDB. Over a two-year period, only three AIS08 injuries were unused. However, less than a third of AIS08 codes encompass 95% of the injuries. Further research is necessary to determine if common codes should be separated into multiple distinct codes to enhance discriminatory ability of AIS.

As obesity rates climb, it is important to study its effects on motor vehicle safety due to differences in restraint interaction and biomechanics. Previous studies have shown that an abdominal seatbelt sign (referred hereafter as seatbelt sign) sustained from motor vehicle crashes (MVCs) is associated with abdominal trauma when located above the anterior superior iliac spine (ASIS). This study investigates whether placement of the lap belt causing a seatbelt sign is associated with abdominal organ injury in occupants with increased body mass index (BMI). We hypothesized that higher BMI would be associated with a higher incidence of superior placement of the lap belt to the ASIS level, and a higher incidence of abdominal organ injury.A retrospective data analysis was performed using 230 cases that met inclusion criteria (belted occupant in a frontal collision that sustained at least one abdominal injury) from the Crash Injury Research and Engineering Network (CIREN) database. Computed tomography (CT) scans were rendered to visualize fat stranding to determine the presence of a seatbelt sign. 146 positive seatbelt signs were visualized. ASIS level was measured by adjusting the transverse slice of the CT to the visualized ASIS level, which was used to determine seatbelt sign location as superior, on, or inferior to the ASIS.Obese occupants had a significantly higher incidence of superior belt placement (52%) vs on-ASIS placement (24%) compared to their normal (27% vs 67%) BMI counterparts (p 0.001). Notable trends included obese occupants with superior placement having less abdominal organ injury incidence than those with on-ASIS belt placement (42% superior placement vs 55% on-ASIS). In non-obese occupants, there was a higher incidence of abdominal organ injury with superior lap belt placement compared to on-ASIS placement counterparts (Normal BMI: 62% vs 41%, Overweight: 57% vs 43%).In CIREN occupants with abdominal injury, those with obesity are more prone to positioning the lap belt superior to the ASIS, though the impact on abdominal injury incidence remains a key point for continued exploration into how occupant BMI affects crash safety and belt design.

OBJECTIVE Obesity has important implications for motor vehicle safety due to altered crash injury responses from increased mass and improper seatbelt placement. Abdominal seatbelt signs (ASBS) above the anterior superior iliac spine (ASIS) in motor vehicle crashes (MVCs) often correlate with abdominopelvic trauma. We investigated the relationship of body mass index (BMI), lap belt placement, and the incidence of abdominopelvic injury using computed tomography (CT) evaluation for subcutaneous ASBS mark and its location relative to the ASIS. METHODS A retrospective analysis of 235 Crash Injury Research and Engineering Network (CIREN) cases and their associated abdominal injuries was conducted. CT Scans were analyzed to visualize fat stranding. 150 positive ASBS were found and their ASBS mark location was classified as superior, on, or inferior to the ASIS. RESULTS Obese occupants had a higher incidence rate of belt placement superior to the ASIS, and occupants with normal BMI had a higher incidence of proper belt placement (p

OBJECTIVE: Prehospital non-transport events occur when emergency medicine service (EMS) providers respond to a scene, but the patient is ultimately not transported to a hospital for evaluation. The objective of this study was to determine the rate of non-transport of pediatric patients who were involved in a motor vehicle collision (MVC) and the factors associated with non-transport decisions. METHODS: We searched the National Emergency Medical Services Information System (NEMSIS) database using ICD-10 mechanism of injury codes to identify cases in which EMS responded to a pediatric occupant (age < 18 years) who had been involved in an MVC. We excluded interfacility transports, scene assists, deaths at the scene, and collisions that occurred outside the US. The outcome of interest was if pediatric patients were not transported to a hospital for evaluation. We performed univariate and multivariate analysis to identify which risk factors were associated with non-transport. We also analyzed regional variation and the reasons recorded for not transporting patients. RESULTS: We identified 92,254 pediatric patients who were evaluated by EMS after an MVC, of which 31,404 (34.0%) were not transported to a hospital for evaluation. In our adjusted analysis, the factors associated with non-transport were age 16 years, male sex, normal Glasgow Coma Scale (GCS = 15), level of training of EMS providers, response time later than 6 a.m., and region of the country. GCS was the most important factor, with only 3.0% (108/3,616) of patients not transported who had abnormal GCS (< 15). In cases of non-transport, 32.7% (10257) were due to patient or caregiver refusal, and 33.3% (10,442) were due to patients being discharged against medical advice. Only 11.5% (3,627) pediatric patients who were not transported were discharged based on an established protocol. CONCLUSIONS: Pediatric patients were not transported after EMS responded to an MVC in approximately one-third of cases, and there was considerable variation in the rate of non-transports based on geographic region, provider level, and time of day. The majority of non-transports occurred because patients were discharged against medical advice or the patient/caregiver refused transport, which may indicate conflicting priorities between EMS providers and patients.

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p

OBJECTIVE Transporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients. METHODS Crash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p

Objective: Older adults make up a rapidly increasing proportion of motor vehicle occupants and previous studies have demonstrated that this population is more susceptible to traumatic injuries. The CDC recommends that patients anticipated to have severe injuries (Injury Severity Score [ISS] ≥ 16) be transported to a trauma center. The recommended target rate for undertriage is ≤ 5% and for overtriage is ≤ 50%. Several regression-based algorithms for injury prediction have been developed in order to predict severe injury in occupants involved in a motor vehicle collision (MVC). The objective of this study to was to determine if the accuracy of regression-based injury severity prediction algorithms decreases for older adults. Methods: Data were obtained from the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) from the years 2000–2015. Adult occupants involved in non-rollover MVCs were included. Regression-based injury risk models to predict severe injury (ISS ≥ 16) were developed using random split-samples with the following variables: age, delta-V, direction of impact, belt status, and number of impacts. Separate models were trained using data from the following age groups: (1) all adults, (2) 15–54 years, (3) ≥45 years, (4) ≥55 years, and (5) ≥65 years. The models were compared using the mean receiver operating characteristic area under curve (ROC-AUC) after 1,000 iterations of training and testing. The predicted rates of overtriage were then determined for each group in order to achieve an undertriage rate of 5%. Results: There were 24,577 occupants (6,863,306 weighted) included in this analysis. The injury prediction model trained using data from all adults did not perform as well when tested on older adults (ROC-AUC: 15–54 years: 0.874 [95% CI: [0.851–0.895]; 45+ years: 0.837 [95% CI: 0.802–869]; 55+ years: 0.821 [95% CI: 0.775–0.864]; and 65+ years: 0.813 [95% CI: 0.754–0.866]). The accuracy of this model decreased in each decade of life. The performance did not change significantly when age-specific data were used to train the prediction models (ROC-AUC: 18–54 years: 0.874 [95% CI: 0.851–0.896]; 45+ years: 0.836 [95% CI: 0.798–0.871]; 55+ years: 0.822 [95% CI: 0.779–0.864]; and 65+ years: 0.808 [95% CI: 0.748–0.868]). In order to achieve an undertriage rate of 5%, the predicted overtriage rate by these models were 50% for occupants 15–54 years, 61% for occupants ≥ 55 years, 70% for occupants ≥ 55 years, and 71% for occupants ≥ 65 years. Conclusion: The results of this study indicate that it is more difficult to accurately predict severe injury in older adults involved in MVCs, which has the potential to result in significant overtriage. This decreased accuracy is likely due to variations in fragility in older adults. These findings indicate that special care should be taken when using regression-based prediction models to determine the appropriate hospital destination for older occupants.

Wikidata is a crowd-sourced knowledge base built by the creators of Wikipedia that applies the principles of neutrality and verifiability to data. In its more than eight years of existence, it has grown enormously, although disproportionately. Some areas are well curated and maintained, while many parts of the knowledge base are incomplete or use inconsistent classifications. Therefore, tools are needed that can use the instantiated data to infer and report structural gaps and suggest ways to address these gaps. We propose a context matrix to automatically suggest potential values for properties. This method can be extended to evaluating the ontology represented by knowledge base. In particular, it could be used to propose types and classes, supporting the discovery of ontological relationships that lend conceptual identification to the content entities. To work with the large, unlabelled data set, we first employ a pipeline to shrink the data to a minimal representation without information loss. We then process the data to build a recommendation model using property frequencies. We explore the results of these models in the context of suggesting type classifications in Wikidata and discuss potential extended applications. As a result of this work, we demonstrate approaches to contextualizing recently-added content in the knowledge base as well as proposing new connections for existing content. Finally, these methods could be applied to other knowledge graphs to develop similar completions for the entities contained therein.

Full body computed tomography (CT) is a widely used clinical evaluation method to detect hidden injury for victims of motor vehicle collisions (MVCs). However, full body CT scans are time consuming and expensive for both healthcare service providers and MVC victims. Injury risk prediction models that support CT scanning decisions are therefore highly desired. Existing studies have implemented logistic regression models to predict injury risk for victims' major body regions, including head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine and lumbar spine. The work presented here involved the application of novel approaches to improve the prediction results. This study focused on examining patient information and crash data for front seat adult passengers using data from the National Automotive Sampling System - Crashworthiness Data System from 2000 to 2015. This dataset is imbued with a large amount of missingness and is highly imbalanced. Various imputation methods were employed in order to preserve the greatest amount of relevant historical data possible. The high imbalance in the data was resolved by the implementation of downsampling and synthetic minority over-sampling technique. Models that were applied in this study include logistic regression, random forests, support vector machines and gradient boosting. Autoencoders were also deployed to generate features of high importance to improve prediction results. The resulting models for all seven regions yielded sensitivities and specificities of at least 96% and 30%, respectively. Overall, these models were developed not to replace physicians' decisions, but to guide their CT scanning decisions.

The first industrial revolution led to mechanical production and steam power; the second, mass production and electrical power; and the third, electronics and computers. Today, as most sectors of the world move forward into the fourth industrial revolution, one centered around data and artificial intelligence (AI), biomedicine finds itself still in the third, lagging behind the rest [1]. Only recently, the exponential growth of technology has facilitated the widespread integration of computers into the biomedical domain from electronization of medical data analysis to automated detection of biomedical images [2–3]. Rather than merely automating time-consuming processes within healthcare, AI stands to reduce medical errors, expand upon the relationships between basic science and clinical medicine, and improve our analysis of existing datasets too large and complex for traditional statistics [3]. Despite these potential benefits, many biomedical facilities are hesitant to incorporate such systems into their practices due to the liability associated with AI making decisions that impact the health of patients [4], such as misdiagnosis (see Rule 8). Additionally, there exists a computational “black box,” a phenomenon describing the difficulty of understanding how AI algorithms arrive at a particular result (see Rule 3). Without a clear means of understanding how these machines generate their output, biomedical facilities are often skeptical of incorporating these “black boxes” into their work practices. As such, the “explainability” issue is an important barrier to overcome before applying these powerful technologies in biomedicine [5]. The lack of understanding around AI and the tantalizing benefits of this new wave of technology suggest the need for professionals in biomedical fields to acquire a basic understanding of AI and its medical applications in order to understand its clinical utility and engage with cutting-edge research. As such, there is a clear need for literature that explains AI in a way that is digestible to professionals in other fields [5]. Without a fundamental understanding of data science models and AI methods, modern biomedical experts who are not well versed in these fundamentals may be intimidated. Introduction to the basics of AI, such as big data analysis, data mining, machine and deep learning, and computer vision, would allow for the expansion of innovative designs in biomedicine. The importance of biomedical involvement in emerging technology is highlighted in the flaws of contemporary electronic medical records (EMR) that are widely used across the healthcare system. The ideal AI adaptation of EMR would be able to facilitate patient care through a variety of features like tracking changes in medical history of a patient and alerting caretakers of concerning health patterns; however, with the current state of EMR, tasks as simple as sharing medical records between healthcare facilities are burdensome. Though it has many virtues, most biomedical professionals agree that the current implementation of EMR is less than ideal, in part because it was developed and implemented with minimal consideration for the flow of information in the biomedical field [6]. The current weaknesses of EMR should serve as a warning, illustrating the importance of biomedical involvement in the deployment of new technologies. When these advancements inevitably make it to the forefront of clinical medicine, biomedical professionals should feel like they are in the driver’s seat rather than helpless passengers along for the ride. We propose the following rules to allow biomedical professionals to attain some measure of control and strap down their panic at the sight of words such as “algorithms,” “AI,” “machine learning” and the like. Rule 1: Don’t panic Computation-based technologies are ubiquitous in our lives, touching almost every facet of our day-to-day interactions. Nevertheless, the vast majority of us do not understand how these systems operate, let alone how to troubleshoot them when problems become apparent. Quickly overwhelmed and frustrated by error messages, constant update reminders, and pop-up advertisements, many of us have an adverse reaction to the increased incorporation of technology into our daily lives. As healthcare begins to adopt a new language unfamiliar to most people, there will be pushback. When there are words that we do not understand, such as “machine learning,” our immediate response is to experience an internal error message and shut down. While it is only natural to have an uneasy and uncomfortable feeling when approaching anything unfamiliar, this sensation can be debilitating and prevent the exploration of the unknown. Now is the time to resist the urge to fall back into something comfortable and learn how to embrace that feeling to allow yourself to grow from new experiences. It should be reassuring that, although many of the terms used in AI seem exotic, they are often deviations on reasonably simple statistical concepts that many biomedical professionals already understand. For example, “a multivariate predictive model using three knots of nonlinearity for continuous values” is fundamentally a linear regression model with some extra bells and whistles. “Deep learning” is a specific method to train neural networks, which are based upon different layers of computational “neurons” that recognize patterns (see Rule 3), much like neurons in the brain firing in response to specific visual inputs [7]. In learning about these new techniques, biomedical professionals will find that they are already familiar with many of the underlying algorithms. Your preexisting knowledge of statistics will serve as the foundation for your understanding of AI, because AI builds on statistics. Both statistics and AI manipulate data with similar algorithms and differ only in the purpose—inference versus prediction, respectively.

OBJECTIVE The clinical evaluation of motor vehicle collision (MVC) victims is challenging and commonly relies on computed tomography (CT) to detect internal injuries. CT scans are financially expensive and each scan exposes the patient to additional ionizing radiation with an associated, albeit low, risk of cancer. Injury risk prediction based on regression modeling has been to be shown to be successful in estimating Injury Severity Scores (ISSs). The objective of this study was to (1) create risk models for internal injuries of occupants involved in MVCs based on CT body regions (head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine, and lumbar spine) and (2) evaluate the performance of these risk prediction models to predict internal injury. METHODS All Abbreviated Injury Scale (AIS) 2008 injury codes were classified based on which CT body region would be necessary to scan in order to make the diagnosis. Cases were identified from the NASS-CDS. The NASS-CDS data set was queried for cases of adult occupants who sought medical care and for which key crash characteristics were all present. Forward stepwise logistic regression was performed on data from 2010-2014 to create models predicting risk of internal injury for each CT body region. Injury risk for each region was grouped into 5 levels: very low (

Previous work has shown that the lap belt moves superior and forward compared to the bony pelvis as body mass index (BMI) increases. The goal of this project was to determine whether the location of lap belt loading is related to BMI for occupants who sustained real-world motor vehicle collisions (MVCs).A national MVC database was queried for vehicle occupants over a 10-year period (2003-2012) who were at least 16 years old, restrained by a 3-point seat belt, sitting in the front row, and involved in a front-end collision with a change in velocity of at least 56 km/h. Cases were excluded if there was not an available computed tomography (CT) scan of the abdomen. CT scans were then analyzed using adipose enhancement of 3-dimensional reconstructions. Scans were assessed for the presence a radiographic seat belt sign (rSBS), or subcutaneous fat stranding due to seat belt loading. In scans in which the rSBS was present, anterior and superior displacement of rSBS from the anterior-superior iliac spine (ASIS) was measured bilaterally. This displacement was correlated with BMI and injury severity.The inclusion and exclusion criteria yielded 151 cases for analysis. An rSBS could definitively be identified in 55 cases. Cases in which occupants were older and had higher BMI were more likely to display an rSBS. There was a correlation between increasing BMI and anterior rSBS displacement (P.01 and P.01, right and left, respectively). There was no significant correlation between BMI and superior displacement of the rSBS (P =.46 and P =.33, right and left, respectively). When the data were examined in terms of relating increasing superior displacement of the lap belt with Injury Severity Scale (P =.34) and maximum Abbreviated Injury Score (AIS) injury severity (P =.63), there was also no significant correlation.The results from this study demonstrated that anterior displacement of the radiographic seat belt sign but not superior displacement increased with higher BMI. These results suggest that obesity may worsen horizontal position but not the vertical position of the lap belt loading during real-world frontal MVCs.

Objective The objective of this study was to describe recent trends in the epidemiology of lawn mower injuries presenting to the Emergency Department in the United States using nationally representative data for all ages. Methods Data for this retrospective analysis were obtained from the U.S. Consumer Product Safety Commission's National Electronic Injury Surveillance System (NEISS), for the years 2005–2015. We queried the system using all product codes under “lawn mowers” in the NEISS Coding Manual. We examined body part injured, types of injuries, gender and age distribution, and disposition. Results There were an estimated 934,394 lawn mower injuries treated in U.S. ED's from 2005 to 2015, with an average of 84,944 injuries annually. The most commonly injured body parts were the hand/finger (22.3%), followed by the lower extremity (16.2%). The most common type of injury was laceration (23.1%), followed by sprain/strain (18.8%). The mean age of individuals injured was 46.5 years, and men were more than three times as likely to be injured as women. Patients presenting to the ED were far more likely to be discharged home after treatment (90.5%) than to be admitted (8.5%). Conclusion Lawn mowers continue to account for a large number of injuries every year in the United States. The incidence of lawn mower injuries showed no decrease during the period of 2005–2015. Preventative measures should take into account the epidemiology of these injuries.

Injuries to the hollow organs of the lower digestive system carry substantial risk of complication due to infection and blood loss, and commonly require invasive abdominal surgery to diagnose and treat. The causes of, and risk factors for, lower abdomen injury in automobile collisions are poorly understood. The goal of this study was to investigate the risk factors and potential mechanisms of hollow-organ, lower abdomen injury in belted automobile occupants in frontal collisions. A field survey data analysis was performed to examine the relationship between various occupant and collision factors and the risk of moderate or greater severity injury (i.e., Abbreviated Injury Scale, AIS 2+) to the small intestine, large intestine, or mesentery among belted occupants involved in frontal collisions. Descriptive and comparative risk factor analyses were performed with data originating from that National Automotive Sampling System Crashworthiness Data System (NASS-CDS) over the years 2000-2011. Multivariable logistic regression models were developed to describe the effects of these factors on hollow-organ injury risk. Potential injury mechanisms were further investigated through in-depth examination of select cases exhibiting hollow-organ injuries from the Crash Investigation Research and Engineering Network (CIREN). The inclusion criteria yielded 25,407 individual cases from NASS-CDS, representing a weighted population of 11,373,358 exposed automobile occupants. Within this dataset, 143 cases (weighted frequency: 7962 occupants) exhibited AIS 2+ injury to hollow abdominal organs. Multivariable regression analysis indicated a statistically significant increased risk of moderate or greater severity injuries to the hollow organs of the abdomen with increased in ΔV (odds ratio (OR) 1.07, 95% confidence interval: 1.06-1.09) and age (OR: 1.03, 1.01-1.06). Albeit non-statistically significant, a positive association between BMI and injury risk was observed, especially among obese individuals (OR: 3.55, 0.82-15.2). No association was observed for gender or seated location within the vehicle. RESULT: from this study indicate that hollow abdominal organ injury is a universal problem in frontal collisions, not confined to a specific gender or seating location. Examination of CIREN cases suggests these types of injuries are associated with direct loading of the lower abdomen by the lap belt, either through poor initial belt positioning or through a "submarining" type of kinematic where the lap belt slips off of the pelvis and loads into the abdomen. Potential countermeasures against hollow-organ abdominal injury should include measures to improve initial lap belt fit, and to retain engagement of the lap belt on the pelvis throughout the collision event. Language: en