Fig. 7.

COC for Classification CIT: % Correct Determinations as a Function of Bootstrap Probability Criteria. Classification operating characteristic (COC) curve representing the percentage of classification-CIT information-present and information-absent determinations that are correct as a function of bootstrap probability criteria. For information-present subjects: X axis is all possible information-present bootstrap probability criteria in 1% increments in descending order, 99.9%, 99%, 98%…2%, 1%, 0.1%. Y axis is the percentage of correct information-present determinations at each criterion, plotted as distance from the bottom of the graph. For information-absent subjects: X axis is all possible information-absent bootstrap probability criteria in 1% increments in ascending order, 0.1%, 1%, 2%…98%, 99%, 99.9%. Y axis is the percentage of correct information-absent determinations at each criterion, plotted as distance from the top of the graph. At each point along the X axis, the difference between the curves represents the percentage of subjects who would be classified correctly by both the corresponding criteria, information present and information absent. The area between the curves (ABC) is the sum of these differences. Perfect discrimination produces an area of 1. Random discrimination produces an area of 0. The error prevention buffer is the range of bootstrap probability criteria where all determinations, information-present and information-absent, would be correct. Setting the respective criteria at any point in this range results in 0% error rate