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Atlas of Pulmonary Pathology

  • ISBN 9781496397553
  • Author: Butt, Yasmeen Mahmood
  • Publ Date: 2021-06-30
  • Edition: 1
  • Binding: Hardback

$460.87
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Closely mirroring the daily sign-out process, Atlas of Pulmonary 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 1,500 high-quality photomicrographs capture the subtle morphologic spectrum of both neoplastic and non-neoplastic lung biopsies. 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 - 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 endoscopic images, photographs of select gross specimens, and medical figures - 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 quiz provides 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.
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