{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:38:43Z","timestamp":1792820323277,"version":"4.5.0"},"reference-count":16,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"NIH","award":["T32GM144274"],"award-info":[{"award-number":["T32GM144274"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The American Surgeon\u2122"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 National Field Triage Guidelines (FTG) assist EMS in prehospital triage; however, its collective performance has never been evaluated using a national database. We aimed to evaluate an FTG surrogate and develop a predictive model to identify patients at risk for serious injury.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Trauma Quality Improvement Program National Trauma Databank (2017-2020) was queried for traumatically injured adults transported via ambulance. Serious injury was defined as ISS \u226516, blood transfusion, angiography with intervention, hemorrhage control surgery, intubation, intracranial pressure monitoring, admission to intensive care, or death. Feature engineering created variables for each FTG criterion. An XGBoost model using prehospital vitals, demographics, and triage criteria was developed with 60\/20\/20 train\/validate\/test split. SHapley Additive exPlanations identified feature importances.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      1\u2009267\u2009039 patients were included; 414\u2009146 (32.7%) had serious injury with a mortality rate of 8.5%. Seriously injured patients were younger and more often male (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). The FTG surrogate achieved 62.9% sensitivity, 58.9% specificity, and AUC 0.609. XGBoost demonstrated superior performance with sensitivity 64.4%, specificity 67.6%, AUC 0.722, while reducing both under-triage (35.6 vs 37.1%) and over-triage rates (32.4 vs 41.1%),\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001. GCS Verbal, sex, prehospital blood pressure, pulse oximetry, and GCS Motor were most influential features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning outperformed a database-derived FTG surrogate on a national trauma database, reducing both under and over-triage rates. The model\u2019s reliance on routinely collected prehospital data supports its potential for EMS use, pending prospective validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00031348261471469","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T01:55:43Z","timestamp":1784944543000},"page":"3014-3020","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?"],"prefix":"10.1177","volume":"92","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9812-6109","authenticated-orcid":false,"given":"R. L.","family":"Wolansky","sequence":"first","affiliation":[{"name":"University of South Florida College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9671-1484","authenticated-orcid":false,"given":"M. A.","family":"Kendall","sequence":"additional","affiliation":[{"name":"University of South Florida College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1721-9124","authenticated-orcid":false,"given":"L.","family":"Hiraldo","sequence":"additional","affiliation":[{"name":"University of South Florida College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Sujka","sequence":"additional","affiliation":[{"name":"University of South Florida College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2610-4233","authenticated-orcid":false,"given":"P. C.","family":"Kuo","sequence":"additional","affiliation":[{"name":"University of South Florida College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"e_1_3_4_2_2","volume-title":"National Hospital Ambulatory Medical Care Survey: 2021 Emergency Department Summary Tables","author":"National Center for Health Statistics","year":"2023","unstructured":"National Center for Health Statistics. National Hospital Ambulatory Medical Care Survey: 2021 Emergency Department Summary Tables; 2023. https:\/\/www.cdc.gov\/nchs\/data\/nhamcs\/web_tables\/2021-nhamcs-ed-web-tables-508.pdf. Accessed 19 January 2026."},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.33204"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000006668"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1136\/tsaco-2024-001729"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsa052049"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1097\/TA.0000000000003627"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2016.11.032"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/acem.14055"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/acem.14056"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.2196\/58740"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.accpm.2025.101507"},{"issue":"1","key":"e_1_3_4_13_2","first-page":"1","article-title":"Guidelines for field triage of injured patients: recommendations of the national expert panel on field triage, 2011","volume":"61","author":"Sasser SM","year":"2012","unstructured":"Sasser SM, Hunt RC, Faul M, et al. Guidelines for field triage of injured patients: recommendations of the national expert panel on field triage, 2011. MMWR Recomm Rep (Morb Mortal Wkly Rep). 2012;61(RR-1):1-20.","journal-title":"MMWR Recomm Rep (Morb Mortal Wkly Rep)"},{"key":"e_1_3_4_14_2","unstructured":"Lundberg SC Lee S. A unified approach to interpreting model predictions. In: Presented at: Proceedings of the 31st international conference on neural information processing systems. California: Long Beach 4-9 December 2017."},{"key":"e_1_3_4_15_2","unstructured":"Wolansky RL. Trauma triage code. 2026. https:\/\/github.com\/rwolansky\/Trauma-Triage"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.52520"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2025.116729"}],"container-title":["The American Surgeon\u2122"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00031348261471469","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00031348261471469","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00031348261471469","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:10:10Z","timestamp":1792818610000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00031348261471469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":16,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["10.1177\/00031348261471469"],"URL":"https:\/\/doi.org\/10.1177\/00031348261471469","relation":{},"ISSN":["0003-1348","1555-9823"],"issn-type":[{"value":"0003-1348","type":"print"},{"value":"1555-9823","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]}}}