Fig. 6.

Reclassification plots illustrating superior performance of GFAP compared to clinical characteristics in predicting traumatic intracranial CT abnormalities. GFAP is added to a logistic regression model that includes clinical parameters from current CT rules. The model with GFAP (y-axis), is compared to the model without GFAP (x-axis). Panels are presented for the overall sample (n=2867), for the strata (ER, Admission, ICU), and for the mild (GCS 13–15), and GCS 15 subgroups. Black dots represent patients with a negative CT scan, red crosses represent patients with a positive CT scan. The percentage of correct reclassification, indicated with green (higher probability of CT positivity if CT is positive, and lower probability of CT positivity if CT is negative), is displayed in the top of each plot.