Table 2. Random forests classification metrics in cross-validation.

Classification task*	Validation method	Accuracy	Specificity	Sensitivity	PPV	NPV
Active GCA versus controls	10-fold CV	95.0%	96.7%	93.3%	96.7%	93.5%
Inactive GCA versus controls	10-fold CV	98.3%	100%	96.7%	100%	96.8%
Active GCA versus inactive GCA	Leave-one-patient-out	51.7%	46.7%	56.7%	51.5%	51.9%

^*Classification tasks were performed using the default configurations of the random forest classifier from the sci-kit learn library (version V.1.3.2) in Python. Classification metrics were calculated as the ratio of total correct predictions to total predictions, as defined for each metric, across each fold of cross-validation method.; PPV, ; NPV, .

GCAgiant cell arteritisNPVnegative predictive valuePPVpositive predictive value