Practice Management Guidelines for the Diagnosis and Management of Injury in the Pregnant Patient: The EAST Practice Management Guidelines Work Group

Trauma during pregnancy has presented very unique challenges over the centuries. From the first report of Ambrose Pare of a gunshot wound to the uterus in the 1600s to the present, there have existed controversies and inconsistencies in diagnosis, management, prognostics, and outcome. Anxiety is heightened by the addition of another, smaller patient. Trauma affects 7% of all pregnancies and requires admission in 4 of 1000 pregnancies. The incidence increases with advancing gestational age. Just over half of trauma during pregnancy occurs in the third trimester. Motor vehicle crashes comprise 50% of these traumas, and falls and assaults account for 22% each. These data were considered to be underestimates because many injured pregnant patients are not seen at trauma centers. Trauma during pregnancy is the leading cause of nonobstetric death and has an overall 6% to 7% maternal mortality. Fetal mortality has been quoted as high as 61% in major trauma and 80% if maternal shock is present. The anatomy and physiology of pregnancy make diagnosis and treatment difficult

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting

One instance of real-time classification results.

<p>The X axis represents time and the Y axis represents probability.</p

Performance of HDPGMs.

<p>Performance of HDPGMs.</p

Comparison between a raw FHR signal and the signal obtained after pre-processing.

<p>Comparison between a raw FHR signal and the signal obtained after pre-processing.</p

Explained variance ratio as a function of number of principle components.

<p>Explained variance ratio as a function of number of principle components.</p

Radiation characterization summary : ACRR 44-inch lead-boron bucket located in the central cavity on the 32-inch pedestal at the core centerline (ACRR-LB44-CC-32-cl).

This document presents the facility-recommended characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields in the Annular Core Research Reactor (ACRR) for the 44-inch-long lead-boron bucket in the central cavity on the 32-inch pedestal at the core centerline. The designation for this environment is ACRR-LB44-CC-32-cl. The neutron, prompt gamma-ray, and delayed gamma-ray energy spectra are presented as well as radial and axial neutron and gamma-ray flux profiles within the experiment area of the bucket. Recommended constants are given to facilitate the conversion of various dosimetry readings into radiation metrics desired by experimenters. Representative pulse and steady-state operations are presented with conversion examples