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This concise, easy-to-read pocket guide offers medical trainees, researchers, and clinicians at every level the perfect resource on Evidence Based Medicine (EBM). Based on the author's many years of experience teaching EBM to medical students and medical residents at Columbia University, this handy title addresses not only all the basic concepts and issues in EBM, but also takes an example-based approach and is replete with numerous illustrations. This brief book provides readers with all the tools needed to tell the good from the bad in healthcare research. It discusses every type of study design, from the assessment of diagnostic tests to clinical trials and meta-analysis. The work also introduces readers to novel methods, such as the Bayesian analysis of clinical trials. In addition, to help readers better retain the information, the guide includes thought-provoking review questions and answers in an appendix. In all, Pocket Evidence-Based Medicine: A Survival Guide for Clinicians a

Practicing evidence-based medicine is widely regarded both as best clinical practice, and as the cornerstone of meeting the ACGME competencies in Practice-Based Learning and Improvement. Training programs recognize the need to teach the skills of EBM and yet struggle with readily available content and guidance on putting together a curriculum. Time frames for delivering curricula in residency can be very tight, often restricted to scattered one hour conferences. This book provides a modular curriculum structure for instructors, with each topic area taking up one section, or one hour of instructional time.Developed over the past 14 years as an introductory course for interns in the internal medicine residency program at Duke, the curriculum will cover core content areas in evidence-based medicine and best teaching practices for them and skills such as literature searching and applying evidence to patients. Most importantly, it will center on actual patient questions and use current lite

1 Introduction2 Representatives2.1 Representative of data sets with one feature2.1.1 Best LS-representativ2.1.2 Best 1-representative2.1.3 Best representative of weighted data2.1.4 Bregman divergences2.2 Representative of data sets with two features2.2.1 Fermat-Torricelli-Weber problem2.2.2 Centroid of a set in the plane2.2.3 Median of a set in the plane 2.2.4 Geometric median of a set in the plane2.3 Representative of data sets with several features2.3.1 Representative of weighted data2.4 Representative of periodic data2.4.1 Representative of data on the unit circle2.4.2 Burn diagram3 Data clustering3.1 Optimal k-partition3.1.1 Minimal distance principle and Voronoi diagram3.1.2 k-means algorithm3.2 Clustering data with one feature3.2.1 Application of the LS-distance-like function3.2.2 The dual problem3.2.3 Least absolute deviation principle3.2.4 Clustering weighted data3.3 Clustering data with two or several features3.3.1 Least squares principle3.3.2 The dual problem3.3.3 Least absol