The health system includes a medical center (with main hospital, children's hospital, and clinic network), school of medicine, school of nursing, and health sciences library. The health system provides inpatient and outpatient care and patient education and conducts medical research and education.Based in Charlottesville, the Health System also operates satellite locations throughout Virginia, in Albemarle, Amherst, Augusta, Campbell, Fluvanna, Louisa, Nelson, and Orange counties.The first medical degrees granted by UVA were awarded in 1828. The University of Virginia Hospital, designed by architect Paul J. Pelz, opened in 1901.The UVA Health System's patient care, research and medical education are frequently ranked highly by several ranking systems. In 2016 and 2017, U.S. News & World Report ranked UVAHS as the number one hospital in Virginia.S.S.S.S.
To evaluate the impact of thoracic spine degeneration in adult spinal deformity (ASD) patients on radiographic outcomes. Primary ASD patients undergoing thoracolumbar fusion with T9–L1 upper instrumented vertebra (UIV) and S1/ilium lower instrumented vertebra were included. Thoracic spine degeneration was assessed radiographically using Kellgren-Lawrence (KL) grading and segmented into T1–T5, T5–T9, and T9–L1 arcs per Lafage criteria. Arc degeneration was defined as ≥ 2 levels with KL grade 3 + in an arc and thoracic spine degeneration as ≥ 1 degenerated arc. Proximal zone degeneration was KL grade 3 + in the two levels above the UIV and distal zone degeneration was KL grade 3 + in unfused thoracic levels outside the proximal zone. Patients with no degenerated levels served as controls. Among 272 patients (mean age 65.1years, 74
Large language models (LLMs) offer promising tools for patient education, yet fixed knowledge cutoffs and hallucination risk limit their clinical utility. Current retrieval-augmented generation (RAG) approaches fail to distinguish between stable clinical knowledge and evolving recommendations. We developed and evaluated bRAGgen, a temporally anchored RAG framework incorporating five modules to enforce clinical protocols for MBS patient education: a semantic knowledge cache, multi-source evidence retrieval with graph-based fusion, uncertainty-aware generation, clinical constraint reranking, and Temporal Fisher Anchoring with Mechanism Selectivity (TFAMS) for adaptive inference. The framework was evaluated using 105 expert-curated free-response questions assessed by a multinational panel of seven specialists (5 surgeons, 2 dietitians) from five countries on a 5-point Likert scale for factuality, clinical relevance, and comprehensiveness. LLM-as-Judge evaluation using ChatGPT-4o provided complementary automated assessment. bRAGgen significantly improved response quality across all five base language models tested (p < 0.001), with large effect sizes for higher-capacity models (Cohen’s d = 0.96–1.01) and moderate effects for smaller models (Cohen’s d = 0.38–0.56) with good inter-rater reliability (Krippendorff’s α = 0.72). The largest gains occurred in safety-critical categories including Risks and Complications (+ 1.84 points) and Mental and Emotional Health (+ 1.84 points), suggesting the framework is most impactful where nuanced clinical judgment is essential. LLM-as-Judge evaluation using ChatGPT-4o demonstrated high concordance with expert ratings (Spearman’s ρ = 0.94). This proof-of-concept study suggests that a multi-module RAG framework with temporal stability anchoring can improve expert-rated LLM response quality for bariatric surgery domain knowledge, though prospective validation in patient-facing settings is needed before clinical implementation.
Background Anti-programmed cell death protein 1 (PD-1) immunotherapy has revolutionized the treatment of stage III and IV melanoma. Real-world data on its resistance is needed to facilitate the development of combinatorial approaches to overcome anti-PD-1 resistance.Objectives To characterize anti-PD-1 resistance and assess whether progressive disease assigned by clinicians is concordant with scan data assessed by independent central reviewers (ICR).Methods A retrospective chart review was conducted in adult patients with stage III/IV melanoma who initiated anti-PD-1 therapy from January 2018 until 12 months before the start of data collection at 22 sites across six countries. Primary resistance and late relapse in the adjuvant setting, and primary, secondary resistance, and late progression in the advanced setting were assigned using Society for Immunotherapy of Cancer definitions. Demographic and clinical characteristics by type of resistance were compared with appropriate univariate tests. Time to resistance (TTR) and overall survival were analyzed using Kaplan-Meier. To compare the concordance of progression assigned by clinicians and ICR, the positive predictive value (PPV) was calculated in a subset of patients.Results Of 981 eligible patients, 738 were included. In the adjuvant setting (n=240), 53 (22.1%) patients developed primary resistance and 60 (25.0%) experienced late relapse. In the advanced setting (n=498), 222 (44.6%), 50 (10.0%), and 64 (12.9%) patients developed primary, secondary resistance, and late progression. Type of resistance significantly differed by country, race, type of BRAF mutation, and PD-L1 expression in both settings; and by sex, disease stage and tumor thickness in the adjuvant setting only (p<0.05). Mean (SD) TTR was 47.7 (1.3) and 24.2 (1.0) months in the adjuvant and advanced setting, respectively. Patients with primary resistance had the poorest overall survival. The PPV of progression assigned by clinicians was 87.2% (95% CI 72.6% to 95.7%).Conclusions This study showed that a substantial proportion of patients with melanoma receiving anti-PD-1 therapy in the adjuvant (47.1%) and advanced (67.5%) settings developed resistance or late relapse/progression, highlighting an unmet medical need. Real-world clinical practice provided a reliable assessment of progression. Factors associated with different types of resistance were identified. Further study is warranted to evaluate their impact on patient risk stratification. (Graphical abstract)
Methicillin-resistant Staphylococcus aureus (MRSA) is a critical public health threat within hospitals as well as long-term care facilities. Better understanding of MRSA risks, evaluation of interventions and forecasting MRSA rates are important public health problems. Existing forecasting models rely on statistical or neural network approaches, which lack epidemiological interpretability, and have limited performance. Mechanistic epidemic models are difficult to calibrate and limited in incorporating diverse datasets. We present CALYPSO, a hybrid framework that integrates neural networks with mechanistic metapopulation models to capture the spread dynamics of infectious diseases (i.e., MRSA) across healthcare and community settings. Our model leverages patient-level insurance claims, commuting data, and healthcare transfer patterns to learn region- and time-specific parameters governing MRSA spread. This enables accurate, interpretable forecasts at multiple spatial resolutions (county, healthcare facility, region, state) and supports counterfactual analyses of infection control policies and outbreak risks. We also show that CALYPSO improves statewide forecasting performance by over 4.5
The prevalence and proliferation of antimicrobial-resistant bacteria is considered one of the critical issues of our time. Wastewater is a habitat for complex microbial communities where bacteria share antimicrobial-resistance genes through horizontal gene transfer. Hospital wastewater plumbing systems are an ideal reservoir for environmental and pathogenic bacteria to interface and exchange antimicrobial-resistance genes. Replacement of contaminated plumbing may be the most intuitive and widely deployed response to the detection and colonization of highly-resistant potentially pathogenic bacteria in hospital sink drains. In this study, we analyzed sink-drain biofilms from six intensive-care patient rooms using shotgun metagenomic sequencing and microbial culture. We show an evident shift in biofilm community structure toward increased abundance of Enterobacteriaceae following plumbing replacement. Higher resistome load and abundance of clinically relevant resistance and typically encountered mobile genes in the newly replaced plumbing was also observed. Taken together, these finding suggest that exchanging contaminated plumbing for new plumbing may actually have the unexpected consequence of increased abundance of Enterobacterales and antimicrobial-resistance genes in the sink drains. Disruption of preexisting complex environmental biofilms may result in an unintended microbial population shifts and a potential subsequent increase in the amount of antimicrobial-resistant Enterobacterales which are targeted for elimination.
