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https://www.selleckchem.com/products/ibmx.html Unintentional injuries are the leading cause of death for young children and many result from them doing injury-risk behaviors in the home. There are a number of questionnaire measures of injury-risk behaviors for children 2 years and older, but none that apply during infancy. The current study addressed this gap. Parents completed the new Infant/Toddler-Injury Behavior Questionnaire when infants were pre-mobile (sitting independently) and mobile (walking independently), with diary measures of injuries and risk behaviors taken continuously throughout this period. Validated questionnaire measures of chaos and routines in the home were also completed. The IT-IBQ showed positive associations with injuries, risk behaviors, and degree of chaos in the home, and was negatively associated with family routines. The results provide evidence for criterion validity and suggest that the new measure holds promise as one that can aid in identifying infants who are likely to engage in injury-risk behaviors.Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1%

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