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Recursive partitioning classification of fragment to optimally discriminate caries status. Terminal nodes are shown as squares, and nonterminal nodes are shown as circles. Each node is labeled as either S-ECC or CF depending on the simple majority of cases within the node. Decision rules are shown on the lines connecting the nodes. This analysis shows that fragments 0018, H7, and 0006 used in a decision tree result in a sensitivity of 96% for S-ECC status and 91% for CF status.
