Atlas of Gastrointestinal Pathology

Closely mirroring the daily sign-out process, Atlas of Gastrointestinal Pathology: A Pattern-Based Approach to Non-Neoplastic Biopsies, 2nd Edition, by Drs. Maryam Pezhouh and Meredith Pittman, is a highly illustrated, efficient guide to accurate diagnosis. This practical reference uses a proven, pattern-based approach to clearly explain how to interpret challenging cases by highlighting red flags in the clinical chart and locating hidden clues in the slides. Useful as a daily scope-side guide, it features numerous clinical and educational features that help you find pertinent information, reach a correct diagnosis, and assemble a thorough and streamlined pathology report. A companion volume for Atlas of Gastrointestinal Pathology is also available and focuses on neoplastic conditions. – More than 1,000 high-quality, full-color images illustrate the full morphologic spectrum of the major patterns of non-neoplastic GI tract pathology, providing abundant and varied examples of each diagnosis. Each image is captioned with key diagnostic considerations and includes callouts showing subtle features and diagnostic clues. – Practical tools throughout the text include: – Tables that emphasize salient clinicopathologic features, management implications, and therapeutic options – Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports – Brief reviews of normal histology that provide contrast to succeeding patterns Pearls and Pitfalls and Near Misses sections with lessons from real life sign-out experience – Frequently Asked Questions sections that discuss common diagnostic dilemmas – Sample Note sections that offer a template of how to sign out cases from the simple to the complex – Comprehensive quizzes that provide experience with high-yield, board-style teaching topics Enrich your eBook reading experience – Read directly on your preferred device(s),such as computer, tablet, or smartphone. – Easily convert to audiobook,powering your content with natural language text-to-speech.