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논문 기본 정보

자료유형
학술저널
저자정보
이희영 (연세대학교) 육현 (연세대학교) 공준석 (연세대학교) 강찬영 (연세대학교) 성실 (연세대학교) 이정훈 (연세대학교) 김호중 (순천향대학교 부천병원) 김상철 (충북대학교병원) 추연일 (충북대학교병원) 전혁진 (춘해보건대학교) 박종찬 (국립과학수사연구원) 최지훈 (국립과학수사연구원) 이강현 (연세대학교)
저널정보
한국자동차안전학회 자동차안전학회지 자동차안전학회지 제10권 제3호
발행연도
2018.9
수록면
20 - 26 (7page)

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초록· 키워드

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It was a pilot study for developing an algorithm to determine the presence or absence of cervical spine injury by analyzing the severity factor of the patients in motor vehicle occupant accidents. From August 2012 to October 2016, we used the KIDAS database, called as Korean In-Depth Accident Study database, collected from three regional emergency centers. We analyzed the general characteristics with several factors. Moreover, cervical spine injury patients were divided into two groups: Group 1 for from Quebec Task Force (hereinafter ‘QTF’) grade 0 to 1, and group 2 for from QTF grade 2 to 4. The score was assigned according to the distribution ratio of cervical spine injured patients compared to the total injured patients, and the cut-off value was derived from the total score by summation of the assigned score of each factors. 987 patients (53.0%) had no cervical spine injuries and 874 patients (47.0%) had cervical spine injuries. QTF grade 2 was found in 171 patients (9.2%) with musculoskeletal pain, QTF grade 3 was found in 38 patients (2.0%) with spinal cord injuries, and QTF grade 4 was found in 119 patients (6.4%) with dislocation or fracture, respectively. We selected the statistically significant factors, which could be affected the cervical spine injury, like the collision direction, the seating position, the deformation extent, the vehicle type and the frontal airbag deployment. Total score, summation of the assigned each factors, 10 was presented as a cut-off value to determine the cervical spine injury. In this study, it was meaningful as a pilot study to develop algorithms by selecting limited influence factors and proposing cut-off value to determine cervical spine injury. However, since the number of data samples was too small, additional data collection and influencing factor analysis should be performed to develop a more delicate algorithm.

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ABSTRACT
1. 서론
2. 연구 방법
3. 연구 결과 및 고찰
4. 결론
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