{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T13:43:49Z","timestamp":1782827029387,"version":"3.54.5"},"publisher-location":"2901 Baxter Road, Ann Arbor, MI, United States","reference-count":47,"publisher":"The Stapp Association","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<div class=\"section abstract\">\n <div class=\"htmlview paragraph\">To estimate risk of concussion, risk functions based on injuries occurring in\n sports are often used. A range of datasets have been used to develop injury risk\n functions for concussion based on either global kinematics or tissue-level\n predictors. Two such datasets are one from American football, and another one\n from Australian football and rugby. These two datasets constitute the largest\n published collections of video-verified concussive cases in sports with known\n kinematics suitable for constructing risk functions. The objective of this study\n was to analyze the differences between two datasets of concussion for injury\n predictions to better understand the influence on injury risk functions. The\n kinematics were applied to the KTH head model and risk functions for different\n kinematic- and tissue-based predictors were developed and compared. The\n accuracy, sensitivity, specificity, and AUC were also compared. The two datasets\n evaluated in this study generated different risk curves. The datasets had some\n similarities such as having no significant difference in resultant linear\n acceleration, but also some differences, for example having a significant\n difference in resultant angular velocity. The Australian cases had relatively\n equally distributed major x-, y-, and z-components for angular velocity while\n the majority (59%) of the NFL cases had a major x-component (coronal plane\n rotation) representing more than 50% of the resultant. The y-component of the\n linear acceleration (lateral direction) was the major component in 64% of the\n Australian cases and 72% of the NFL cases. The two datasets, from Australian\n football\/rugby, and American football, generated different injury risk curves\n with a lower 50% risk of concussion for the Australian dataset. This indicates\n that the choice of data as input for the development of injury risk functions is\n important. Therefore, it is necessary to improve methodology with focus on\n sampling methods and reliable\/valid data collection.<\/div>\n <\/div><\/jats:p>","DOI":"10.4271\/2026-22-0004","type":"proceedings-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:41Z","timestamp":1781740841000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Concussion Risk Functions Generated from Two Different\n Datasets"],"prefix":"10.4271","author":[{"given":"Madelen","family":"Fahlstedt","sequence":"first","affiliation":[{"name":"Division of Neuronic Engineering, KTH Royal Institute of"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyang","family":"Meng","sequence":"additional","affiliation":[{"name":"Division of Neuronic Engineering, KTH Royal Institute of"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Declan","family":"Patton","sequence":"additional","affiliation":[{"name":"Center for Injury Research and Prevention, Children\u2019s Hospit"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew S.","family":"McIntosh","sequence":"additional","affiliation":[{"name":"Center for Injury Research and Prevention, Children\u2019s Hospit"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svein","family":"Kleiven","sequence":"additional","affiliation":[{"name":"Center for Injury Research and Prevention, Children\u2019s Hospit"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2796","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref0","doi-asserted-by":"crossref","unstructured":"Anderson , E.D. , \n \n Giudice , J.S. , \n \n Wu , T. , \n \n Panzer , M.B. \n \n et al. \n Predicting Concussion Outcome by Integrating\n Finite Element Modeling and Network Analysis Front. 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