Table 4.

Performance of Logistic Regression Models Trained Using Acute SRC Metrics

	Cat. 2: Symptoms ≥1 week		Cat. 3: Symptoms ≥2 weeks
	Training		Validation	Training	Validation
Clin-SCAT	0.688 (0.667–0.709)		0.672 (0.439–0.905)	0.672 (0.657–0.687)	0.558 (0.360–0.757)
Clin-SAC	0.610 (0.596–0.624)		0.522 (0.370–0.675)	0.558 (0.531–0.585)	0.417 (0.192–0.641)
Clin-BESS	0.537 (0.517–0.558)		0.461 (0.213–0.710)	0.670 (0.651–0.689)	0.733 (0.543–0.924)
QSM-WM	0.542 (0.531–0.554)		0.511 (0.335–0.688)	0.697 (0.676–0.718)	0.717 (0.471–0.962)
QSM-GM	0.523 (0.510–0.536)		0.422 (0.211–0.634)	0.756 (0.739–0.772)	0.783 (0.597–0.970)
QSM-ALL	0.596 (0.578–0.614)		0.500 (0.308–0.692)	0.765 (0.750–0.779)	0.633 (0.410–0.857)
Clin–All	0.676 (0.661–0.690)		0.589 (0.476–0.702)	0.618 (0.604–0.632)	0.520 (0.305–0.735)
All Metrics	0.701 (0.681–0.721)		0.633 (0.483–0.783)	0.872 (0.853–0.890)	0.760 (0.598–0.922)

Cat., Category; SCAT, Sport Concussion Assessment Tool; SAC, Standardized Assessment of Concussion; BESS, Balance Error Scoring System; QSM, quantitative susceptibility maps; WM, white matter; GM, gray matter.

AUC metrics are reported as means (95% confidence interval) for the 100 bootstrapped models computed for each metric. In addition, precision recall and validation performance results are reported.