Introduction: In the OPTIMISMM trial, pomalidomide/bortezomib/dexamethasone (PVd) significantly prolonged median progression-free survival (PFS) versus bortezomib/dexamethasone (Vd) in lenalidomide-exposed relapsed and refractory multiple myeloma (RRMM). We report final overall survival (OS) and updated efficacy analyses.
Methods: Adults with RRMM who had 1-3 prior regimens, including lenalidomide (≥ 2 cycles), were assigned (1:1) to PVd or Vd.
Motivation: The expansion of genetic association data from genome-wide association studies has increased the importance of methodologies like Polygenic Risk Scores (PRS) and Mendelian Randomization (MR) in genetic epidemiology. However, their application is often impeded by complex, multi-step workflows requiring specialized expertise and the use of disparate tools with varying data formatting requirements. Existing solutions are frequently standalone packages or command-line based-largely due to dependencies on tools like PLINK-limiting accessibility for researchers without computational experience.
View Article and Find Full Text PDFBackground: As digitalization continues to advance globally, the health care sector, including dental practice, increasingly recognizes social media as a vital tool for health care promotion, patient recruitment, marketing, and communication strategies.
Objective: This study aimed to investigate the use of social media and assess its impact on enhancing dental care and practice among dental professionals in the Philippines.
Methods: A cross-sectional survey was conducted among dental practitioners in the Philippines.
Background: Prenatally transmitted viruses can cause severe damage to the developing brain. There is unexplained variability in prenatal brain injury and postnatal neurodevelopmental outcomes, suggesting disease modifiers. Of note, prenatal Zika infection can cause a spectrum of neurodevelopmental disorders, including congenital Zika syndrome.
View Article and Find Full Text PDFAccurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports.
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