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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Junior doctors make clinical decisions regularly; therefore, they need to adequately interpret the evidence supporting these decisions. Patients can be harmed if clinical treatments are supported by biased or unreliable evidence. Systematic reviews that contain meta-analyses of randomized controlled trials are a relatively low-biased type of evidence to support clinical interventions. ⋯ In this article, doctors are informed about potential methodological and ethical issues in systematic reviews that contain a meta-analysis that are sometimes not easily identified or even overlooked by the current tools developed to assess their methodological quality or risk of bias. The article presents a discussion of topics related to data extraction, accuracy in reporting, reproducibility, heterogeneity, quality assessment of primary studies included in the systematic review, sponsorship, and conflict of interest. It is expected that the information reported will be useful for junior doctors when they are reading and interpreting evidence from systematic reviews containing meta-analyses of therapeutic interventions, mainly those doctors unfamiliar with methodological principles.
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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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Bar charts of numerical data, often known as dynamite plots, are unnecessary and misleading. Their tendency to alter the perception of mean's position through the within-the-bar bias and their lack of information on the distribution of the data are two of numerous reasons. The machine learning tool, Barzooka, can be used to rapidly screen for different graph types in journal articles.We aim to determine the proportion of original research articles using dynamite plots to visualize data, and whether there has been a change in their use over time. ⋯ Our results show that the use of dynamite plots in surgical research has decreased over time; however, use remains high. More must be done to understand this phenomenon and educate surgical researchers on data visualization practices.