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Traffic Injury Prevention Volume 23, 2022 - Issue sup1: Peer-Reviewed Journal for the 66th Annual Scientific Conference of the Association for the Advancement of Automotive Medicine (AAAM)
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Short Communications from the AAAM 66th Annual Conference

Comparisons of head injury risk prediction methods to field data in far-side impacts

Karan Devanea Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North CarolinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-6891-2119View further author information
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Fang-Chi Hsub Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North CarolinaORCID Iconhttps://orcid.org/0000-0002-3310-184XView further author information
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Bharath Koyaa Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North CarolinaORCID Iconhttps://orcid.org/0000-0003-3784-2706View further author information
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Matthew Davisc Elemance, LLC, Winston-Salem, North CarolinaView further author information
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Ashley A. Weavera Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North CarolinaORCID Iconhttps://orcid.org/0000-0002-4383-0106View further author information
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F. Scott Gayzika Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North Carolina;c Elemance, LLC, Winston-Salem, North CarolinaORCID Iconhttps://orcid.org/0000-0002-7443-5424View further author information
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Berkan Guleyupogluc Elemance, LLC, Winston-Salem, North CarolinaORCID Iconhttps://orcid.org/0000-0002-8974-6292View further author information
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Pages S189-S192 | Published online: 03 Oct 2022
 
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Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

F. Scott Gayzik and Matthew Davis are members and Ashley A. Weaver is a consultant of Elemance, LLC., which distributes academic and commercial licenses for the use of GHBMC-owned computational human body models.

Additional information

Funding

This work was funded by the United States Council for Automotive Research (USCAR) under grant ID: 21-2262-USC.

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