Whether or not the ROC curve is based on a single measurement or repeated measurements, the choice of a cutoff depends on the trade-off between sensitivity and specificity. Ask the doctor whether sensitivity is more important than specificity or vice-versa and how much more important one is than another. For example, the sensitivity at or above level A of TNM if the proportion of all true pregnancies that are detected; the specificity below level A of TNM is the proportion of those who are truly not pregnant who are declared as not pregnant.
Or, ask the doctor whether level A of TNM yields an acceptable positive predictive value of the test: What proportion of the tests positive for pregnancy are associated with actual pregnancies? Unlike sensitivity and specificity, predictive values depend on the prevalence of the outcome (=pregnancy) in the population being tested; if pregnancy is rare in this population, positive predictive values are lower so that many positive tests are detected among those who are NOT pregnant.
I can't answer your other questions about differences in estimates of the area-under-the-curve between PROC LOGISTIC and PROC GLIMMIX. In general, multiple tests showing consistent results should improve sensitivity and specificity. Since you have no time indexes on your tests, you probably should consider either compound-symmetry or unrestricted variance-covariance matrices that do not depend on time.
