Predicting mortality among septic patients presenting to the emergency department-a cross sectional analysis using machine learning.

Background: Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (ED) using machine learning.

Methods: Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to sepsis.

Checkpoint inhibitors for malignant melanoma: a systematic review and meta-analysis.

Background And Objectives: Rates of malignant melanoma are continuing to increase, and until recently effective treatments were lacking. However, since 2011 three immunotherapeutic agents, known as checkpoint inhibitors, have been approved. This review aims to establish whether these three drugs - ipilimumab, nivolumab, and pembrolizumab - offer greater efficacy and tolerability compared to control interventions (placebo, immunotherapy, or chemotherapy) in patients with stage III or IV unresectable cutaneous melanoma.