Background: Maternity and neonatal services are rapidly changing in Australia because of evolving needs of the community and patient population. Clinical practice guidelines focused on early interventions and prevention strategies can decrease risk for preventable negative health outcomes in this population. However, despite the existence of several clinical practice guidelines, their translation into practice remains problematic for healthcare services.
View Article and Find Full Text PDFAccurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem services. Although there has been research on simultaneously fitting biomass equations for tree components, rather few studies focus on simultaneously predicting AGB and BGB at stand-level while maintaining additivity. We developed innovative Deep Learning Additive Models (DLAMs) for the simultaneous predictions of stand-level AGB, BGB, and ABGB integrating forest stand, ecological, and environmental factors as predictive covariates and compared them with conventional weighted nonlinear seemingly unrelated regression (WNSUR) and multivariate adaptive regression splines (MARS).
View Article and Find Full Text PDFBackground: In Nepal, gastric cancer was the second most common cause of cancer deaths in males and the fifth most common cause of cancer deaths in females in 2020. Although gastric cancer is a significant public health problem, there have been no studies undertaken in Nepal to determine the survival and predictors of gastric cancer survival.
Methods: A retrospective cohort study of gastric cancer patients at Bhaktapur Cancer Hospital in Kathmandu Valley, Nepal.