In this example, the screening test identified 174 individuals as at risk for illness; 75 (43%) are correctly classified, and 99 (57%) are incorrectly classified. Thus, even with high specificity, more individuals identified by the screening test are false positive than true positive. Furthermore, as specificity decreases, the proportion that are false positive rapidly increases. For example, if the specificity were 90%, then the screening test would identify 1,065 individuals as at risk, 990 (93%) of whom would be false positive. Dr. Davidson and colleagues reported a "validated specificity" of 99.7% for their screening tool. Sensitivity and specificity are not absolute values but vary with cutoff scores. Measurement error and other factors make it unlikely that the screening tool would have perfect or near-perfect specificity when used to screen for schizophrenia in other populations. Even with near-perfect specificity (99.7%), the test predicted that 103 individuals would become ill, of whom 30 individuals (29%) were false positive.