Search

The article informs on book awards including American Indian Youth Literature Awards, Coretta Scott King Book Awards, and Coretta Scott King - John Steptoe Award for New Talent.

Student health visitors led a Mini-MEND project for families with young children as part of their public health nursing course. The project gave a framework for the students and staff members from the children's centre to stimulate an awareness of health needs and facilitate health enhancing activities in terms of healthy eating and parenting. Reflection on the initiative identified application of the principles of health visiting and the domains of specialist community public health nursing, yet the pressures of current health visiting workloads mean that having developed public health skills to use with groups and communities, they may not be used by newly-qualified health visitors. [ABSTRACT FROM AUTHOR]

PittUDT, a recursive partitioning decision tree algorithm for predicting urine culture (UC) positivity based on macroscopic and microscopic urinalysis (UA) parameters, was developed in support of a broader system-wide diagnostic stewardship initiative to increase appropriateness of UC testing. Reflex algorithm training utilized results from 19,511 paired UA and UC cases (26.8% UC positive); the average patient age was 57.4 years, and 70% of samples were from female patients. Receiver operating characteristic (ROC) analysis identified urine white blood cells (WBCs), leukocyte esterase, and bacteria as the best predictors of UC positivity, with areas under the ROC curve of 0.79, 0.78, and 0.77, respectively. Using the held-out test data set (9,773 cases; 26.3% UC positive), the PittUDT algorithm met the prespecified target of a negative predictive value above 90% and resulted in a 30 to 60% total negative proportion (true-negative plus false-negative predictions). These data show that a supervised rule-based machine learning algorithm trained on paired UA and UC data has adequate predictive ability for triaging urine specimens by identifying low-risk urine specimens, which are unlikely to grow pathogenic organisms, with a false-negative proportion under 5%. The decision tree approach also generates human-readable rules that can be easily implemented across multiple hospital sites and settings. Our work demonstrates how a data-driven approach can be used to optimize UA parameters for predicting UC positivity in a reflex protocol, with the intent of improving antimicrobial stewardship and UC utilization, a potential avenue for cost savings., Competing Interests: The authors declare no conflict of interest.