Our objectives were to use a quantitative literature review to explore dietary and feed factors influencing apparent total-tract digestibility of dry matter (DMD), crude protein (CPD), neutral detergent fiber (NDFD), ether extract (EED), non-structural carbohydrates (NSCD), non-fiber carbohydrates (NFCD), and residual organic matter (rOMD) in equine diets, and to assess their contributions to digestible energy (DE) supplies. Data from 54 studies were modeled using linear mixed-effect regressions, with publication as a random effect to account for study variability. For each nutrient, five models were derived with explanatory variables including: dry matter intake (DMI; % BW/day) and DM (% as-fed), and dietary components (CP, organic matter, EE, NDF, acid detergent fiber, NSC, starch, and NFC as % of DM), and feed types (forage, non-forage fiber, legumes, cereal, and oil proportions).
View Article and Find Full Text PDFTotal hip arthroplasty (THA) is a widely performed surgical procedure in the United States, but disparities in THA outcomes related to hospital-level factors, such as safety-net burden, are underexplored. This study expands on previous research by analyzing multicenter, multistate data from 2015 to 2020 to investigate the impact of hospital safety-net burden-defined as the proportion of services billed to Medicaid and uninsured patients-on THA outcomes. This study is a retrospective analysis using data from the State Inpatient Databases for Florida, Kentucky, Maryland, New York, Washington, New Jersey, and North Carolina.
View Article and Find Full Text PDFBackground: Approximately 20% of global tuberculosis incidence is attributable to undernutrition, increasing to more than a third in India. Targeting nutritional interventions to tuberculosis-affected households is a policy priority, but understanding of epidemiological and economic impacts is limited. We aimed to estimate the population-level epidemiological and economic effect of such an intervention.
View Article and Find Full Text PDFArtificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.
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