Brit J Hosp Med
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A 21-year-old gentleman presented with low responsiveness and an unwitnessed tonic-clonic seizure. A 3-day history of fevers, headaches, and poor sleep was reported. He was initially treated for meningoencephalitis. ⋯ Ergo, this encourages an early multidisciplinary approach in presentations of headaches and seizures as clinical suspicion for CVST is high. Ultimately, this will appropriately identify patients for neuroimaging with computed tomography/magnetic resonance venogram. Furthermore, 5-year follow-up is presented in this case highlighting the importance of long-term follow-up in view of variable long-term complications that remain difficult to predict.
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Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational study. The primary objective of the model was to enhance clinical decision-making by providing a more precise assessment of ENAD risk. ⋯ The area under the ROC curve was 0.904 (95% confidence interval: 0.817-0.997). Conclusion The artificial intelligence bowel sounds auscultation system enhances the assessment of gastrointestinal function in AP patients undergoing EN and facilitates the construction of an ENAD predictive model. The model demonstrates good predictive efficacy, offering an objective basis for precise intervention timing in ENAD management.
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We report a case of a 6-year-old boy with autism spectrum disorder presenting with new-onset squint and 'ptosis' following a recent infection. Clinical examination revealed ataxia and areflexia alongside a dilated pupil poorly reactive to light. Subsequently, his eye movements deteriorated to near-complete ophthalmoplegia at 1-week review. ⋯ The clinical triad of progressive ophthalmoplegia, areflexia and areflexia alongside albuminocytologic dissociation led to the diagnosis of Miller Fisher syndrome. The patient was commenced on intravenous immunoglobulin and his symptoms showed significant improvement. We use this interesting case to provide context for key learning points about diagnosing Miller Fisher syndrome in children.
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Aims/Background: Portal vein tumor thrombus (PVTT) is a common complication of primary hepatocellular carcinoma (HCC). HCC typically infiltrates intrahepatic vessels, particularly the portal vein, leading to the formation of PVTT, marking advanced-stage HCC and correlating with poor prognosis. PVTT often complicates local treatment strategies such as surgical resection and affects the efficacy of interventions. ⋯ In addition, no adverse effects were observed during the treatment process. However, despite the manageable safety profile demonstrated by combination therapy, further clinical research is needed to validate its long-term efficacy and safety. Conclusion: Camrelizumab + apatinib produced satisfactory efficacy and safety among the HCC patients with PVTT, providing clinical evidence for future treatment.
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Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. ⋯ Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.