Association of patient- and hospital-level predictors with patterns of initial treatment in patients with Rheumatoid Arthritis: findings from a national cohort study.

Objectives: To update the first-line conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) prescribing pattern, describe change and variation across demographical and geographical factors in the Rheumatoid arthritis (RA) population, and identify individual and hospital factors associated with it.

Methods: This retrospective cohort study included newly diagnosed RA adult patients from 1 May 2018-1 April 2023 in the UK. We used adjusted multinomial logistic regression with random effect to explore associations with different first-line csDMRAD prescription and to account for hospital-level clustering.

Whole grain modified Nova ultra-processed food (UPF) definitions: a cross-sectional analysis of the impact on cardiometabolic risk measures when excluding high whole-grain foods from the UPF category in Australia.

Ultra-processed foods (UPF), defined using the Nova classification system, are associated with increased chronic disease risk. More recently, evidence suggests the UPF subgroup of whole-grain breads and cereals is in fact linked with reduced chronic disease risk. This study aimed to explore associations of cardiometabolic risk measures with Nova UPF intake versus when foods with ≥25% or ≥50% whole grain are excluded from the definition.

Family Caregiver Knowledge in the Outpatient Management of Pediatric Tracheostomy-Related Emergencies.

Tracheostomy-related emergencies (TREs) contribute significantly to preventable mortality. The retention of caregiver knowledge and skills acquired through simulation-based training (SBT) is unknown. This study aimed to assess the management of TREs by caregivers who did and did not receive SBT.

AURA: Accelerating drug discovery with accuracy, utility, and rank-order assessment for data-driven decision making.

Biopharmaceutical companies generate a wealth of data, ranging from in silico physicochemical properties and machine learning models to both low and high-throughput in vitro assays and in vivo studies. To effectively harnesses this extensive data, we introduce a statistical methodology facilitated by Accuracy, Utility, and Rank Order Assessment (AURA), which combines basic statistical analyses with dynamic data visualizations to evaluate endpoint effectiveness in predicting intestinal absorption. We demonstrated that various physicochemical properties uniquely influence intestinal absorption on a project-specific basis, considering factors like intestinal efflux, passive permeability, and clearance.