Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Despite its importance in improving patient care, the state of published emergency medicine (EM) research is poorly understood. The countries of origin, methodological characteristics, sources of funding, and ongoing trends in this research are unknown. Knowledge of these characteristics has important policy, research, and clinical implications for academic EM. ⋯ Emergency medicine research output is increasing worldwide. The United States is the largest producer of EM research, only a small fraction of which is supported by the NIH. The majority of research published by emergency researchers is published in non-EM journals.
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Comparative Study
Prospective study of the clinical features and outcomes of emergency department patients with delayed diagnosis of pulmonary embolism.
The authors hypothesized that emergency department (ED) patients with a delayed diagnosis of pulmonary embolism (PE) will have a higher frequency of altered mental status, older age, comorbidity, and worsened outcomes compared with patients who have PE diagnosed by tests ordered in the ED. ⋯ In this single-center study, the diagnosis of PE was frequently delayed and outcomes of patients with delayed diagnosis were worse than those of patients with PE diagnosed in the ED.
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An emergency medicine (EM) clerkship can provide a medical student with a unique educational experience. The authors sought to describe the current experiential curriculum of the EM clerkship, along with methods of evaluation, feedback, and grading. ⋯ Medical students are exposed to a variety of didactic lectures and procedure labs but have similar experiences regarding shift length and work hours. Methods of evaluation of clinical performance vary across clinical sites.
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In part 1 of this series, the authors describe the importance of incomplete data in clinical research, and provide a conceptual framework for handling incomplete data by describing typical mechanisms and patterns of censoring, and detailing a variety of relatively simple methods and their limitations. In part 2, the authors will explore multiple imputation (MI), a more sophisticated and valid method for handling incomplete data in clinical research. This article will provide a detailed conceptual framework for MI, comparative examples of MI versus naive methods for handling incomplete data (and how different methods may impact subsequent study results), plus a practical user's guide to implementing MI, including sample statistical software MI code and a deidentified precoded database for use with the sample code.
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Missing data are commonly encountered in clinical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce study power, and lead to invalid conclusions. ⋯ In part 1, the authors will describe relatively simple approaches to handling missing data, including complete-case analysis, available-case analysis, and several forms of single imputation, including mean imputation, regression imputation, hot and cold deck imputation, last observation carried forward, and worst case analysis. In part 2, the authors will describe in detail multiple imputation, a more sophisticated and valid method for handling missing data.