Internal and emergency medicine
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Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. ⋯ We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.
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To examine the risk factors for severe pain upon discharge from the emergency department, assuming appropriate pharmacological treatment of pain, in order to improve pain relief in emergency departments and reduce the risk of potential chronic pain. ⋯ SOFTER IV Project clinical identification number: NCT04916678.
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Emergency Departments (EDs) across Italy use different triage systems, which vary from region to region. This study aimed to assess whether nurses working in different EDs assign triage codes in a similar and standardized manner. ⋯ There is a high degree of subjectivity in triage code assignment by ED nurses. In the interest of equitable care for patients, this variability within the same country is hardly acceptable.