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. 2022 May 6;22:74. doi: 10.1186/s12873-022-00609-5

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Over/under triage rates of various decision trees. + Each marker represents a different CHAID decision tree, as listed in Table 4. *Model A variables were: MPDS dispatch codes. Model B variables were: MPDS dispatch codes, anyone trapped, vulnerable road user, airbag deployed, atmosphere, road surface. Model C variables were anyone trapped, vulnerable road user, anyone not ambulant, atmosphere, accident type. Model D variables were: MPDS dispatch codes, anyone trapped, vulnerable road user, anyone aged ≥ 75 years, day of the week, single v. multi-vehicle, airbag deployed, atmosphere, road surface, lighting, accident type. Model E variables were: MPDS dispatch codes, anyone trapped, vulnerable road user, anyone aged ≥ 75 years, day of the week, single v. multi-vehicle, airbag deployed, atmosphere, road surface, lighting, accident type