Patients requiring emergency airway management may be at greater risk of acute hypoxemic events because of underlying lung pathology, high metabolic demands, insufficient respiratory drive, obesity, or the inability to protect their airway against aspiration. Emergency tracheal intubation is often required before complete information needed to assess the risk of procedural hypoxia is acquired (i.e., arterial blood gas level, hemoglobin value, or chest radiograph). During pre-oxygenation, administering high-flow nasal oxygen in addition to a non-rebreather face mask can significantly boost the effective inspired oxygen. Similarly, with the apnea created by rapid sequence intubation (RSI) procedures, the same high-flow nasal cannula can help maintain or increase oxygen saturation during efforts to secure the tube (oral intubation). Thus, the use of nasal oxygen during pre-oxygenation and continued during apnea can prevent hypoxia before and during intubation, extending safe apnea time, and improve first-pass success attempts. We conducted a literature review of nasal-cannula apneic oxygenation during intubation, focusing on two components: oxygen saturation during intubation, and oxygen desaturation time. We performed an electronic literature search from 1980 to November 2017, using PubMed, Elsevier, ScienceDirect, and EBSCO. We identified 14 studies that pointed toward the benefits of using nasal cannula during emergency intubation.
As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize identities, they also contain face related information such as sex, age, and pose of the face. The networks are not trained to learn these attributes. We introduce expressivity as a measure of how much a feature vector informs us about an attribute, where a feature vector can be from internal or final layers of a network. Expressivity is computed by a second neural network whose inputs are features and attributes. The output of the second neural network approximates the mutual information between feature vectors and an attribute. We investigate the expressivity for two different deep convolutional neural network (DCNN) architectures: a Resnet-101 and an Inception Resnet v2. In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw. Additionally, we studied the changes in the encoding of facial attributes over training iterations. We found that as training progresses, expressivities of yaw, sex, and age decrease. Our technique can be a tool for investigating the sources of bias in a network and a step towards explaining the network's identity decisions.
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