We define sensitivity as the ability of the algorithm to identify the poor prognosis patients as poor-prognosis, specificity as the ability of the algorithm to identify the good prognosis patients as good-prognosis, and accuracy as the ability of the algorithm to accurately identify the patients as either good or poor prognosis patients.
Secondly, we separately filtered search returns for longitudinal observational studies in Embase and Medline by using "prognosis-specificity" database specific filters and in Web of Science by using text words relating to the study design.
Sensitivity refers to correctly classifying poor prognosis patients and specificity refers to correctly classifying good prognosis patients.
However, although changes in PSA levels do give a general prognosis they lack specificity for the individual (Nelius and Filleur, 2009).
Notably, based on both profiles, the 50 patients having the highest scores only included four poor prognosis cases (92% specificity) while the expected number was 13.9.
In the test set, this validation showed an accuracy of 92.3%, sensitivity of 85.57% (true-poor prognosis rate) and specificity of 100% (true-fair prognosis rate) for all the left out samples (test samples).
On day 3 of antibiotic therapy, a decrease in C-reactive protein levels by 0.31 or more from the previous day's level was a marker of good prognosis (sensitivity, 0.75; specificity, 0.85).
Given that the sample size used in this study is relatively small, conclusions cannot be drawn about the relationship of these miRNAs to prognosis or their specificity to the TNBC subtype.
A decrease in CRP levels by 0.31 or more from the previous day's concentration was a marker of good prognosis (sensitivity, 0.75; specificity, 0.85; positive likelihood ratio, 4.87; negative likelihood ratio, 0.30; negative predictive value, 0.92; positive predictive value, 0.61).
The molecular classification of breast cancer is of high importance due to patient subtype-specific prognoses and the subtype-specificity of potential drug targets [ 9, 10].
Moreover, we found that a cutoff point of 14 optimizes sensitivity (the probability that the test correctly detects subjects with a poor prognosis of functioning) and specificity (the probability that the test correctly detects subjects with a good prognosis of functioning).
