Table 2.

Analysis of predictive performance for five prediction models.

Prediction models	Performance (95% CI)
	AUC	Accuracy	Precision	Sensitivity	F1 Score	Specificity	PPV	NPV	Brier score
Thumb-up RF model	0.8802 [0.8605–0.8998]	0.759 [0.690–0.820]	0.744 [0.680–0.810]	0.788 [0.700–0.880]	0.766 [0.700–0.830]	0.729 [0.630–0.820]	0.744 [0.650–0.830]	0.775 [0.680–0.870]	0.14
Collection RF model	0.7685 [0.7398–0.7972]	0.688 [0.620–0.760]	0.700 [0.630–0.770]	0.659 [0.560–0.760]	0.679 [0.610–0.750]	0.718 [0.620–0.810]	0.700 [0.600–0.800]	0.678 [0.580–0.770]	0.197
Share RF model	0.7872 [0.7467–0.8276]	0.718 [0.650–0.790]	0.723 [0.660–0.790]	0.706 [0.610–0.800]	0.714 [0.650–0.780]	0.729 [0.630–0.820]	0.723 [0.630–0.820]	0.713 [0.620–0.810]	0.19
Comment RF model	0.8077 [0.7885–0.8268]	0.706 [0.640–0.770]	0.697 [0.630–0.770]	0.729 [0.630–0.820]	0.713 [0.640–0.780]	0.682 [0.580–0.780]	0.697 [0.600–0.790]	0.716 [0.620–0.810]	0.182
Comment XGboost model	0.797 [0.7634–0.8306]	0.694 [0.620–0.760]	0.699 [0.630–0.770]	0.682 [0.580–0.780]	0.690 [0.620–0.760]	0.706 [0.610–0.800]	0.699 [0.600–0.800]	0.690 [0.590–0.790]	0.188