Mathematically, it is equal to the ratio of

true negatives over

true negatives plus false negatives (see figure 1).

0% (36 of 50) were

true negatives (Figure 2, G) and 28% (14 of 50) were considered false negatives.

Test Efficiency(*) Conventional Tests Criteria ICL TV True positive 9 7 9

True negative 16 17 20 False positive 4 3 0 False negative 1 3 1 Sensitivity, % 90 70 90 Specificity, % 65 85 100 Positive predictive value, % 56 70 100 Negative predictive value, % 93 85 95 Efficiency, % 73 80 97 Tests ICL + TV ICL or TV True positive 6 10

True negative 20 17 False positive 0 3 False negative 4 0 Sensitivity, % 60 100 Specificity, % 100 85 Positive predictive value, % 100 77 Negative predictive value, % 83 100 Efficiency, % 87 90 (*) ICL indicates intracytoplasmic lumen; TV, transgressing vessels.

Myeloid Immature Atypical Analyzer Blasts Precursors Gran Lymph XE-2100 True positive, n 18 104 77 8

True negative, n 438 303 327 441 False positive, n 25 53 75 23 False negative, n 5 26 7 14 Sensitivity, % 78 80 92 36 Specificity, % 95 85 81 95 Efficiency, % 94 84 83 92 NE-8000 True positive, n 15 61 27 9

True negative, n 411 241 356 433 False positive, n 52 115 46 31 False negative, n 8 69 57 13 Sensitivity, % 65 47 32 41 Specificity, % 89 68 89 93 Efficiency, % 88 62 79 91

Studies suggest that the Octava Pink test confirms

true negative mammography results with high accuracy, while identifying the presence of cancer in more than half of the cases when the mammography result is a false negative and cancer is actually present.

Phe], HP, and PKU), performance measures were based on two numbers: the true positives (HP, PKU) and the

true negatives (CTRL, [FP.

1 True Positives 9 7 12

True Negatives 5 8 10 False Positives 2 0 (0.

In an ideal world, only true positives and

true negatives occur.

Four measures typically help in evaluating classifiers: The number of cases the classifier got correct--True Positives and

True negatives and the number of cases the classifier got incorrect -False positives and false negatives.

The true positives (TP) and

true negatives (TN) are correct classifications.

Calculation:

True Negatives / (

True Negatives + False Positives) [Brunette, 1996].

Because almost all of the data which would provide evidence for the validity of a negative clinical test (both

true negatives and false negatives) was not collected, this research is unable to provide a valid estimate of specificity.