Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Bouncebacks. Pediatrics is the third book of the Bouncebacks! series and describes 28 real pediatric cases from the emergency department. Each chapter is authored by the attending physician and/or resident who cared for these patients. ⋯ Each chapter concludes with a summary and analysis of the clinical scenario, take home teaching points for the reader that highlight key concepts, and tips for avoiding diagnostic errors. This article is protected by copyright. All rights reserved.
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Review Meta Analysis
Incidence of Adverse Events in Adults Undergoing Procedural Sedation in the Emergency Department: A Systematic Review and Meta-analysis.
This was a systematic review and meta-analysis to evaluate the incidence of adverse events in adults undergoing procedural sedation in the emergency department (ED). ⋯ Serious adverse events during procedural sedation like laryngospasm, aspiration, and intubation are exceedingly rare. Quantitative risk estimates are provided to facilitate shared decision-making, risk communication, and informed consent.
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The Manchester Acute Coronary Syndromes (MACS) decision rule has been shown to be a powerful diagnostic tool in emergency department (ED) patients with suspected acute coronary syndromes (ACS). It has the potential to improve system efficiency by identifying patients suitable for discharge after a single blood draw for high-sensitivity troponin and heart-type fatty acid-binding protein (h-FABP) analysis at presentation to the ED. The objective was to externally validate the MACS decision rule and establish its diagnostic accuracy as a discharge tool in a new set of prospectively recruited ED patients. ⋯ In this prospectively recruited cohort of ED patients with suspected ACS, the MACS decision rule identifies a significant proportion of patients who are suitable for immediate discharge after a single blood draw at presentation, with a very low risk of MACE at 30 days. This study externally validates previous findings that the MACS rule is a powerful diagnostic tool in this setting. A randomized controlled trial to establish the utility of the rule in an everyday clinical setting is justified.
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The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. ⋯ A hybrid NLP and machine learning automated classification system continues to show promise in coding free-text electronic clinical data. For complex outcomes, it can reliably identify negative reports, but manual review of positive reports may be required. As such, it can still streamline data collection for clinical research and performance improvement.