Postgraduate medical journal
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This article reviews the correlation between presepsin and sepsis and the resulting acute respiratory distress syndrome (ARDS). ARDS is a severe complication of sepsis. Despite the successful application of protective mechanical ventilation, restrictive fluid therapy, and neuromuscular blockade, which have effectively reduced the morbidity and mortality associated with ARDS, the mortality rate among patients with sepsis-associated ARDS remains notably high. ⋯ Recent studies have demonstrated significant variations in presepsin (PSEP) levels between patients with sepsis and those without, particularly in the context of ARDS. Moreover, these studies have revealed substantially elevated PSEP levels in patients with sepsis-associated ARDS compared to those with nonsepsis-associated ARDS. Consequently, PSEP emerges as a valuable biomarker for identifying patients with an increased risk of sepsis-associated ARDS and to predict in-hospital mortality.
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The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Additive exPlanation (SHAP), to develop a predictive model for critically ill patients. ⋯ A transparent ML model for predicting outcomes in critically ill patients using SHAP methodology is feasible and effective. SHAP values significantly improve the explainability of ML models.
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Temporal trends and risk factors of perioperative cardiac events (PCEs) in patients over 80 years old with coronary artery disease (CAD) undergoing noncardiac surgery are still unclear. ⋯ The incidence and independent risk factors of PCEs in patients over 80 years old with CAD undergoing noncardiac surgery showed significant rising trends over the last 9-year period.
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We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. ⋯ The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.
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Generative conversational artificial intelligence (AI) has huge potential to improve medical education. This pilot study evaluated the possibility of using a 'no-code' generative AI solution to create 2D and 3D virtual avatars, that trainee doctors can interact with to simulate patient encounters. ⋯ By providing trainees with realistic scenarios, this technology allows trainees to practice answering patient questions regardless of actor availability, and indeed from home. Furthermore, the use of a 'no-code' platform allows clinicians to create customized training tools tailored to their medical specialties. While overall successful, this pilot study highlighted some of the current drawbacks and limitations of generative conversational AI, including the risk of outputting false information. Additional research and fine-tuning are required before generative conversational AI tools can act as a substitute for actors and peers.