Evaluation of a Child Abuse Screen Performed by Nurses Among Young Children with Fractures Seen in a Pediatric Emergency Department.

Aims: To assess institutional compliance with, and test characteristics of, a child abuse screen performed by emergency department (ED) nurses for children <5 years old who were diagnosed with fractures.

Methods: A secondary analysis of a retrospective observational study of children 0-5 years old with fractures seen at a pediatric ED between January 2018 and April 2023 was performed. We analyzed demographics, ED visit data, and results of the nurse-completed abuse screen.

Leveraging Unlabeled Electroencephalographic Data to Predict Neurological Recovery for Comatose Patients Following Cardiac Arrest.

In response to the 2023 George B. Moody PhysioNet Challenge, we propose an automated, unsupervised pre-training approach to boost the performance of models that predict neurologic outcomes after cardiac arrest. Our team, (BrownBAI), developed a model architecture consisting of three parts: a pre-processor to convert raw electroencephalograms (EEGs) into two-dimensional spectrograms, a three-layer convolutional neural network (CNN) encoder for unsupervised pre-training, and a time series transformer (TST) model.

The Crossroads between Workforce and Education.

Concern is growing among industry leaders that students may not be obtaining the necessary skills for entry into the labor market. To gain an understanding of the perceived disconnect in the skill set of graduates entering the health information workforce, a survey was developed to examine the opinions of educators and employers related to graduate preparedness. The concern related to graduate preparedness is supported by findings in this research study, in which those working in industry and those in academia noted a disconnect between academic training and preparedness to enter the labor market.