Background: The Pediatric Emergency Care Applied Research Network (PECARN) head trauma clinical decision rules informed the development of algorithms that risk stratify the management of children based on their risk of clinically important traumatic brain injury (ciTBI). We aimed to determine the rate of ciTBI for each PECARN algorithm risk group in an external cohort of patients and that of ciTBI associated with different combinations of high- or intermediate-risk predictors. Methods: This study was a secondary analysis of a large multicenter prospective data set, including patients with Glasgow Coma Scale scores of 14 or 15 conducted in Australia and New Zealand. We calculated ciTBI rates with 95% confidence intervals (CIs) for each PECARN risk category and combinations of related predictor variables. Results: Of the 15,163 included children, 4,011 (25.5%) were aged <2 years. The frequency of ciTBI was 8.5% (95% CI = 6.0%–11.6%), 0.2% (95% CI = 0.0%–0.6%), and 0.0% (95% CI = 0.0%–0.2%) in the high-, intermediate-, and very-low-risk groups, respectively, for children <2 years and 5.7% (95% CI = 4.4%–7.2%), 0.7% (95% CI = 0.5%–1.0%), and 0.0% (95% CI = 0.0%–0.1%) in older children. The isolated high-risk predictor with the highest risk of ciTBI was “signs of palpable skull fracture” for younger children (11.4%, 95% CI = 5.3%–20.5%) and “signs of basilar skull fracture” in children ≥2 years (11.1%, 95% CI = 3.7%–24.1%). For older children in the intermediate-risk category, the presence of all four predictors had the highest risk of ciTBI (25.0%, 95% CI = 0.6%–80.6%) followed by the combination of “severe mechanism of injury” and “severe headache” (7.7%, 95% CI = 0.2%–36.0%). The very few children <2 years at intermediate risk with ciTBI precluded further analysis. Conclusions: The risk estimates of ciTBI for each of the PECARN algorithms risk group were consistent with the original PECARN study. The risk estimates of ciTBI within the high- and intermediate-risk predictors will help further refine clinical judgment and decision making on neuroimaging.