• Miranda Chacon, Catherine W Liu, Loralai Crawford, Hadassah Polydore, Tiffany Ting, Derek Wakeman, and Nicole A Wilson.
• Department of Surgery (Chacon), University of Rochester Medical Center, Rochester, NY.
• J. Am. Coll. Surg. 2024 Aug 1; 239 (2): 134144134-144.

BackgroundAssigning trauma team activation (TTA) levels for trauma patients is a classification task that machine learning models can help optimize. However, performance is dependent on the "ground-truth" labels used for training. Our purpose was to investigate 2 ground truths, the Cribari matrix and the Need for Trauma Intervention (NFTI), for labeling training data.Study DesignData were retrospectively collected from the institutional trauma registry and electronic medical record, including all pediatric patients (age <18 years) who triggered a TTA (January 2014 to December 2021). Three ground truths were used to label training data: (1) Cribari (Injury Severity Score >15 = full activation), (2) NFTI (positive for any of 6 criteria = full activation), and (3) the union of Cribari+NFTI (either positive = full activation).ResultsOf 1,366 patients triaged by trained staff, 143 (10.47%) were considered undertriaged using Cribari, 210 (15.37%) using NFTI, and 273 (19.99%) using Cribari+NFTI. NFTI and Cribari+NFTI were more sensitive to undertriage in patients with penetrating mechanisms of injury (p = 0.006), specifically stab wounds (p = 0.014), compared with Cribari, but Cribari indicated overtriage in more patients who required prehospital airway management (p < 0.001), CPR (p = 0.017), and who had mean lower Glasgow Coma Scale scores on presentation (p < 0.001). The mortality rate was higher in the Cribari overtriage group (7.14%, n = 9) compared with NFTI and Cribari+NFTI (0.00%, n = 0, p = 0.005).ConclusionsTo prioritize patient safety, Cribari+NFTI appears best for training a machine learning algorithm to predict the TTA level.Copyright © 2024 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.

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