A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach.
Hee-Young LeeKang Hyun LeeOh Hyun KimHyun YoukJoon Seok KongChan Young KangDoo Ruh ChoiYeon Il ChooDong Ku KangPublished in: Traffic injury prevention (2023)
Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury.