MADYMO simulation of children in cycle accidents: a novel approach in risk assessment.

Head injuries are a significant cause of death and injury to child cyclists both on and off the road. Current evaluations of the effectiveness of cycle helmets rely on simplified mechanical testing or the analysis of aggregated accident statistics. This paper presents a direct evaluation of helmet efficacy by using computational modelling to simulate a range of realistic accident scenarios, including loss of control, collision with static objects and vehicle impact. A 6-year-old cyclist was modelled (as a Hybrid III 6-year-old dummy), in addition to a typical children's bicycle and a vehicle using the MADYMO dynamics software package. Simulations were performed using ranges of cyclist position, cycle speed and vehicle speed with and without a helmet that meets current standards. Wearing a cycle helmet was found to reduce the probability of head injuries, reducing the average probability of fatality over the scenarios studied from 40% to 0.3%. Similarly, helmet wearing reduced the probability of neck injuries (average probability of fatality reduced from 11% to 1%). There was no evidence that helmet wearing increased the severity of brain or neck injuries caused by rotational accelerations; in fact these were slightly reduced. Similarly, there was no evidence that increased cycling speed, such as might result from helmet related risk compensation, increased the probability of head injury.

Three quantitative approaches to the diagnosis of abdominal pain in children: practical applications of decision theory.

BACKGROUND/PURPOSE: The authors compared 3 quantitative methods for assisting clinicians in the differential diagnosis of abdominal pain in children, where the most common important endpoint is whether the patient has appendicitis. Pretest probability in different age and sex groups were determined to perform Bayesian analysis, binary logistic regression was used to determine which variables were statistically significantly likely to contribute to a diagnosis, and recursive partitioning was used to build decision trees with quantitative endpoints. METHODS: The records of all children (1,208) seen at a large urban emergency department (ED) with a chief complaint of abdominal pain were immediately reviewed retrospectively (24 to 72 hours after the encounter). Attempts were made to contact all the patients' families to determine an accurate final diagnosis. A total of 1,008 (83%) families were contacted. Data were analyzed by calculation of the posttest probability, recursive partitioning, and binary logistic regression. RESULTS: In all groups the most common diagnosis was abdominal pain (ICD-9 Code 789). After this, however, the order of the most common final diagnoses for abdominal pain varied significantly. The entire group had a pretest probability of appendicitis of 0.06. This varied with age and sex from 0.02 in boys 2 to 5 years old to 0.16 in boys older than 12 years. In boys age 5 to 12, recursive partitioning and binary logistic regression agreed on guarding and anorexia as important variables. Guarding and tenderness were important in girls age 5 to 12. In boys age greater than 12, both agreed on guarding and anorexia. Using sensitivities and specificities from the literature, computed tomography improved the posttest probability for the group from.06 to.33; ultrasound improved it from.06 to.48; and barium enema improved it from.06 to.58. CONCLUSIONS: Knowing the pretest probabilities in a specific population allows the physician to evaluate the likely diagnoses first. Other quantitative methods can help judge how much importance a certain criterion should have in the decision making and how much a particular test is likely to influence the probability of a correct diagnosis. It now should be possible to make these sophisticated quantitative methods readily available to clinicians via the computer.