Annals of emergency medicine
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This 2023 Clinical Policy from the American College of Emergency Physicians is an update of the 2008 “Clinical Policy: Neuroimaging and Decisionmaking in Adult Mild Traumatic Brain Injury in the Acute Setting.” A writing subcommittee conducted a systematic review of the literature to derive evidence-based recommendations to answer the following questions: 1) In the adult emergency department patient presenting with minor head injury, are there clinical decision tools to identify patients who do not require a head computed tomography? 2) In the adult emergency department patient presenting with minor head injury, a normal baseline neurologic examination, and taking an anticoagulant or antiplatelet medication, is discharge safe after a single head computed tomography? and 3) In the adult emergency department patient diagnosed with mild traumatic brain injury or concussion, are there clinical decision tools or factors to identify patients requiring follow-up care for postconcussive syndrome or to identify patients with delayed sequelae after emergency department discharge? Evidence was graded and recommendations were made based on the strength of the available data. Widespread and consistent implementation of evidence-based clinical recommendations is warranted to improve patient care.
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The emergency department serves as a critical access point to the health system for many patients, especially those with limited resources. Screening for disease or risk factors for poor health outcomes can potentially improve both individual and population health. Screening initiatives should focus on evidence-based strategies and take local epidemiology and ED capacity into consideration. ⋯ They should also be financially sustainable for those involved. Screening can identify patients who can then be counseled, provided with prophylaxis or treatment, or referred to external resources. Through screening and intervention, the ED can serve as a vital contributor to individual and population health.
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Causal diagrams are used in biomedical research to develop and portray conceptual models that accurately and concisely convey assumptions about putative causal relations. Specifically, causal diagrams can be used for both observational studies and clinical trials to provide a scientific basis for some aspects of multivariable model selection. This methodology also provides an explicit framework for classifying potential sources of bias and enabling the identification of confounder, collider, and mediator variables for statistical analyses. ⋯ We present numeric examples of effect estimate calculations and miscalculations based on analyses of the well-known Framingham Heart Study. Clinical researchers can use causal diagrams to improve their understanding of complex causation relations to determine accurate and valid multivariable models for statistical analyses. The Framingham Heart Study dataset and software codes for our analyses are provided in Appendix E1 (available online at http://www.annemergmed.com) to allow readers the opportunity to conduct their analyses.