@article{ref1,
title="In search of the truth: choice of ground-truth for predictive modeling of trauma team activation in pediatric trauma",
journal="Journal of the American Academy of Surgeons",
year="2024",
author="Chacon, Miranda and Liu, Catherine W. and Crawford, Loralai and Polydore, Hadassah and Ting, Tiffany and Wakeman, Derek and Wilson, Nicole A.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: Assigning trauma team activation levels for trauma patients is a classification task that machine learning models can help optimize. However, performance is dependent upon the &quot;ground-truth&quot; labels used for training. Our purpose was to investigate two ground-truths, the Cribari matrix and the Need for Trauma Intervention (NFTI), for labeling training data. STUDY DESIGN: Data was retrospectively collected from the institutional trauma registry and electronic medical record, including all pediatric patients (age <18 y) who triggered a trauma team activation (1/2014 - 12/2021). 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). <br><br>RESULTS: Of 1,366 patients triaged by trained staff, 143 (10.47%) were considered under-triaged using Cribari, 210 (15.37%) using NFTI, and 273 (19.99%) using Cribari+NFTI. NFTI and Cribari+NFTI were more sensitive to under-triage in patients with penetrating mechanisms of injury (p = 0.006), specifically stab wounds (p = 0.014), compared to Cribari, but Cribari indicated over-triage in more patients who required prehospital airway management (p < 0.001), CPR (p = 0.017), and who had mean lower GCS scores on presentation (p < 0.001). The mortality rate was higher in the Cribari over-triage group (7.14%, n = 9) compared to NFTI and Cribari+NFTI (0.00%, n = 0, p = 0.005). <br><br>CONCLUSION: To prioritize patient safety, Cribari+NFTI appears best for training a machine learning algorithm to predict trauma team activation level.<p /> <p>Language: en</p>",
language="en",
issn="1072-7515",
doi="10.1097/XCS.0000000000001044",
url="http://dx.doi.org/10.1097/XCS.0000000000001044"
}