Tricia A. Numan: Atlas of Gynecologic Pathology, Gebunden

Klappentext

Closely mirroring the daily sign-out process, Atlas of Gynecologic Pathology: A Pattern Based Approach 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. ?More than 1, 000 high-quality photomicrographs capture the subtle morphologic spectrum and if applicable, pertinent immunohistochemical staining patterns encountered in gynecologic pathology, including variations of tumor types, non-tumor pathology, artifacts, and normal histology. Each image is captioned with key diagnostic considerations and includes call-outs showing subtle features and diagnostic clues.?Practical tools throughout the text include: ?Tables that emphasize salient clinicopathologic features, management implications, and therapeutic options?Discussions of how and when to incorporate molecular tools?Checklists for key elements of the diagnostic approach and sample notes for inclusion in pathology reports?Relevant diagnostic algorithms?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 synthesize complicated or especially challenging topics?Comprehensive quiz provides experience with high-yield, board-style teaching topicsEnrich 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.