Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Clinical guidelines have supported outpatient treatment of low-risk pulmonary embolism (PE) since 2014, but adoption of this practice has been slow. Direct oral anticoagulant (DOAC) therapy for venous thromboembolism (VTE) is now as common as vitamin K antagonist treatment, but data are sparse regarding outcomes for patients with low-risk PE treated with DOACs as outpatients. We conducted a systematic review of literature on outcomes of outpatient management for PE, including comparisons to inpatient treatment and differences by anticoagulant class. ⋯ Among patients with low-risk PE treated as outpatients, few patients experienced major adverse outcomes such as mortality, recurrent VTE, or major bleeding within 90 days.
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Meta Analysis
Machine Learning versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review.
Having shown promise in other medical fields, we sought to determine whether machine learning (ML) models perform better than usual care in diagnostic and prognostic prediction for emergency department (ED) patients. ⋯ Our review suggests that ML may have better prediction performance than usual care for ED patients with a variety of clinical presentations and outcomes. However, prediction model reporting guidelines should be followed to provide clinically applicable data. Interventional trials are needed to assess the impact of ML models on patient-centered outcomes.
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Containment of the coronavirus disease 2019 (COVID-19) pandemic requires the public to change behavior under social distancing mandates. Social media are important information dissemination platforms that can augment traditional channels communicating public health recommendations. The objective of the study was to assess the effectiveness of COVID-19 public health messaging on Twitter when delivered by emergency physicians and containing personal narratives. ⋯ Emergency physicians sharing personal narratives on Twitter are perceived to be more effective at communicating COVID-19 health recommendations compared to federal officials sharing impersonal guidance.