Second trimester pregnancy loss and preterm delivery may be considered an obstetrical syndrome. A multifactorial approach to the diagnosis of true cervical insufficiency is paramount. Surgical modification of the cervix benefits those with at least 3 second trimester losses or preterm deliveries, those with 2 early second trimester losses when no other cause for loss is identified, and those with a previous second trimester loss or preterm birth with ultrasound findings of a short cervix defined as less than 25 mm. Multifetal pregnancies do not benefit from cerclage and causes harm in those with ultrasound or physical examination identified cervical changes.
The question of whether or not structural test measurements can be used to predict functional or system Fmax, has been studied for many years. This paper presents a data learning approach to study the question. Given Fmax values and structural delay measurements on a set of sample chips, we propose a method called conformity check whose goal is to select a subset of conformal samples such that a more reliable predictor can be built on. Our predictor consists of two models, a conformal model that decides on a given chip if its Fmax is predictable or not, and a prediction model that outputs the predicted Fmax based on results obtained from structural test measurements. We explain the data learning methodology and study various data learning techniques using frequency data collected on a highperformance microprocessor design.
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