Background: The COVID-19 pandemic has been accompanied by an “infodemic,” where the rapid spread of misinformation has exacerbated public health challenges. Traditional fact-checking methods, though effective, are time-consuming and resource-intensive, limiting their ability to combat misinformation at scale. Large language models (LLMs) such as GPT-4 offer a more scalable solution, but their susceptibility to generating hallucinations—plausible yet incorrect information—compromises their reliability. Objective: This study aims to enhance the accuracy and reliability of COVID-19 fact-checking by integrating a retrieval-augmented generation (RAG) system with LLMs, specifically addressing the limitations of hallucination and context inaccuracy inherent in stand-alone LLMs. Methods: We constructed a context dataset comprising approximately 130,000 peer-reviewed papers related to COVID-19 from PubMed and Scopus. This dataset was integrated with GPT-4 to develop multiple RAG-enhanced models: the naïve RAG, Lord of the Retrievers (LOTR)–RAG, corrective RAG (CRAG), and self-RAG (SRAG). The RAG systems were designed to retrieve relevant external information, which was then embedded and indexed in a vector store for similarity searches. One real-world dataset and one synthesized dataset, each containing 500 claims, were used to evaluate the performance of these models. Each model’s accuracy, F1-score, precision, and sensitivity were compared to assess their effectiveness in reducing hallucination and improving fact-checking accuracy. Results: The baseline GPT-4 model achieved an accuracy of 0.856 on the real-world dataset. The naïve RAG model improved this to 0.946, while the LOTR-RAG model further increased accuracy to 0.951. The CRAG and SRAG models outperformed all others, achieving accuracies of 0.972 and 0.973, respectively. The baseline GPT-4 model reached an accuracy of 0.960 on the synthesized dataset. The naïve RAG model increased this to 0.972, and the LOTR-RAG, CRAG, and SRAG models achieved an accuracy of 0.978. These findings demonstrate that the RAG-enhanced models consistently maintained high accuracy levels, closely mirroring ground-truth labels and significantly reducing hallucinations. The CRAG and SRAG models also provided more detailed and contextually accurate explanations, further establishing the superiority of agentic RAG frameworks in delivering reliable and precise fact-checking outputs across diverse datasets. Conclusions: The integration of RAG systems with LLMs substantially improves the accuracy and contextual relevance of automated fact-checking. By reducing hallucinations and enhancing transparency by citing retrieved sources, this method holds significant promise for rapid, reliable information verification to combat misinformation during public health crises.

Multiple myeloma (MM) is a hematological malignancy that is consistently preceded by an asymptomatic condition, monoclonal gammopathy of undetermined significance (MGUS). Disparities by age, gender, and race/ethnicity in both MGUS and MM are well-established. However, it remains unclear whether these disparities can be explained by increased incidence of MGUS and/or accelerated progression from MGUS to MM. Here, we fit a mathematical model to nationally representative data from the United States and showed that the difference in MM incidence can be explained by an increased incidence of MGUS among male and non-Hispanic Black populations. We did not find evidence showing differences in the rate of progression from MGUS to MM by either gender or race/ethnicity. Our results suggest that screening for MGUS among high-risk groups (e.g., non-Hispanic Black men) may hold promise as a strategy to reduce the burden and MM health disparities.

To determine the effect of donor hepatitis C virus (HCV) infection on kidney transplant (KT) outcomes in the era of direct-acting antiviral (DAA) medications, we examined 68,087 HCV-negative KT recipients from a deceased donor between March 2015 and May 2021. A Cox regression analysis was used to estimate adjusted hazard ratios (aHRs) of KT failure, incorporating inverse probability of treatment weighting to control for patient selection to receive an HCV-positive kidney (either nucleic acid amplification test positive [NAT+, n = 2331] or antibody positive (Ab+)/NAT- [n = 1826]) based on recipient characteristics. Compared with kidney from HCV-negative donors, those from Ab+/NAT- (aHR = 0.91; 95% confidence interval [CI], 0.75-1.10) and HCV NAT+ (aHR = 0.89; 95% CI, 0.73-1.08) donors were not associated with an increased risk of KT failure over 3 years after transplant. Moreover, HCV NAT+ kidneys were associated with a higher 1-year estimated glomerular filtration (63.0 vs 61.0 mL/min/1.73 m², P = .007) and lower risk of delayed graft function (aOR = 0.76; 95% CI, 0.68-0.84) compared with HCV-negative kidneys. Our findings suggest that donor HCV positivity is not associated with an elevated risk of graft failure. The inclusion of donor HCV status in the Kidney Donor Risk Index may no longer be appropriate in contemporary practice.

Introduction: This study systematically reviewed scientific evidence concerning the influence of green space on obesity in China. Methods: Keyword and reference search was conducted in PubMed, Web of Science, Scopus, EBSCO, and CNKI. Predetermined selection criteria included study designs: experimental and observational studies; subjects: people of all ages; exposures: green space (i.e., any open land partly or entirely covered with grass, trees, shrubs, or other vegetation); outcomes: body weight status (e.g., body mass index [BMI], overweight, or obesity); and country: China. Results: Ten studies met the selection criteria and were included in the review. All studies adopted a cross-sectional design. Overall greenness measures were found to be inversely associated with BMI, overweight, and obesity in most included studies. Street greenness, which measures the perceived greenness at the eye level on streets, was found to be inversely associated with BMI and obesity. By contrast, mixed results were observed for the relationship between green space accessibility and weight outcomes. Air quality was found to mediate the relationship between greenness and obesity. The influence of green space on obesity tended to vary by residents’ gender, age, and socioeconomic status. Boys, women, older residents, and those with lower education or household income were more likely to benefit from greenness exposure. Conclusion: The literature on green space exposure in relation to obesity in China remains limited. Longitudinal and quasi-experimental studies are warranted to assess the causal link between green space and obesity. Future measures should better capture the self-perception, quality, and attractiveness of green space. The underlying pathways through which green space affects residents’ weight outcomes should be further elucidated.

Monoclonal gammopathy of undetermined significance (MGUS) is one of the most prevalent premalignant conditions and carries approximately 1% annual risk of transformation to multiple myeloma or other lymphoproliferative disorders.¹ To date, 2 studies have reported shorter survival in clinically diagnosed US patients with MGUS compared with matched counterparts without MGUS. Nonetheless, these studies included predominantly White participants and did not adjust for comorbidities.^2,3 The observed shorter survival in patients with MGUS may be driven by underlying health conditions that led to incidental diagnosis of asymptomatic MGUS. We analyzed a nationally representative sample screened for MGUS to evaluate overall survival of populations with MGUS compared with the general US population.