Brit J Hosp Med
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Primary glomerulonephritis comprises several renal-limited diseases that can cause haematoproteinuria, chronic kidney disease, nephrosis and end stage kidney disease. The most common of these are IgA nephropathy (IgAN), primary membranous nephropathy (PMN), Focal Segmental Glomerulosclerosis (FSGS) and Minimal Change Disease (MCD). Although rare, these diseases cause a significant burden to health care systems, given the high cost of treating End Stage Kidney Disease (ESKD) with dialysis or transplantation. ⋯ However, recent advances in understanding of how these diseases evolve has led to the introduction of novel therapeutic agents. Trials are underway or have recently completed that have huge implications for the standard of care for the primary glomerulonephritidies, and should dramatically reduce the number of patients who progress onto end stage kidney disease. This article reviews the international Kidney Disease Improving Global Outcomes (KDIGO) guidelines for the treatment of IgAN, PMN, FSGS and MCD, as well as recent research on pathogenesis and treatment.
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Academic hospitalists play an integral role in the day-to-day care of hospitalized patients, education and research. They are well-positioned to engage in scholarly and research activities and inform clinical practice. ⋯ Participation in these endeavors not only foster scholarly growth but also enhances career satisfaction for hospitalists. Therefore, there is a need to explore and implement feasible strategies to equip hospitalists with the knowledge and resources necessary to generate scholarship and promote academic growth within the field.
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Clinical reasoning is fundamental for effective clinical practice. Traditional consultation models for teaching clinical reasoning or conventional approaches for teaching students how to make a diagnosis or management plan that rely on learning through observation only, are increasingly recognised as insufficient. There are also many challenges to supporting learners in developing clinical reasoning over time as well as across different clinical presentations and contexts. ⋯ Diagnostic errors may be due to cognitive biases but also, in a majority of cases, due to a lack of clinical knowledge. Therefore, effective educational strategies to develop clinical reasoning include identifying learners' knowledge gaps, using worked examples to prevent cognitive overload, promoting the use of key features and practising the construction of accurate problem representations. Deliberate reflection on diagnostic justification is also recommended, and overall, contributes to a growing number of evidence-based and theory-driven educational interventions for reducing diagnostic errors and improving patient care.
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Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overview of key clinical use cases such as detection, classification, segmentation and radiomics. ⋯ We provide a broad theoretical framework for assessing the clinical effectiveness of medical imaging artificial intelligence, including appraising internal validity and generalisability of studies, and discuss barriers to clinical translation. Finally, we highlight future directions of travel within the field including multi-modal data integration, federated learning and explainability. By having an awareness of these issues, clinicians can make informed decisions about adopting artificial intelligence for medical imaging, improving patient care and clinical outcomes.