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Journal Article

Citation

Schoell SL, Doud AN, Weaver AA, Talton JW, Barnard RT, Winslow JE, Stitzel JD. Accid. Anal. Prev. 2016; 98: 149-156.

Affiliation

Virginia Tech-Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC, USA; Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.aap.2016.10.001

PMID

27723516

Abstract

BACKGROUND: Occult injuries are not easily detected and can be potentially life-threatening. The purpose of this study was to quantify the perceived occultness of the most frequent motor vehicle crash injuries according to emergency medical services (EMS) professionals. STUDY DESIGN: An electronic survey was distributed to 1,125 EMS professionals who were asked to quantify the likelihood that first responders would miss symptoms related to a particular injury on a 5-point Likert scale. The Occult Score for each injury was computed from the average of all the survey responses and normalized to be a continuous metric ranging from 0 to 1 where 0 is a non-occult (highly apparent on initial presentation) injury and 1 is an occult (unapparent on initial presentation) injury.

RESULTS: Overall, 110,671 survey responses were collected. The Occult Score ranged from 0 to 1 with a mean, median, and standard deviation of 0.443, 0.450, and 0.233, respectively. When comparing the Occult Score of an injury to its corresponding AIS severity, there was no relationship between the metrics. When stratifying by body region, injury type, and AIS severity, it was evident that AIS 2-4 abdominal injuries with lacerations, hemorrhage, or contusions were perceived as the most occult injuries.

CONCLUSIONS: Timely triage is key to reduce the morbidity and mortality associated with occult injuries. The Occult Score developed in this study to describe the predictability of an injury in a motor vehicle crash will be used as part of a larger effort, including incorporation into an advanced automatic crash notification (AACN) algorithm to detect crash conditions associated with a patient's need for prompt treatment at a trauma center.

Copyright © 2016 Elsevier Ltd. All rights reserved.


Language: en

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