Postgraduate medical journal
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Physicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients. ⋯ Although not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.
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To collect validity evidence for the chest tube insertion (CTI) test mode on the medical simulation application Touch Surgery. This was done by using Messick's contemporary framework. ⋯ A robust validity argument was constructed for the CTI test mode, which can be implemented in surgical curricula to assess learners' cognitive skills prior to hands-on simulation practice.