Pocket Neurology

Pocket Neurology, a bestseller in the Pocket Notebook series, delivers highly relevant neurologic clinical information in an easily portable source. Drs. Marcelo Matiello, Michael P. Bowley, Sahar F. Zafar, and M. Brandon Westover edit this book by overseeing the work of current neurology residents, fellows and neurology attendings at Harvard Medical School who provide must-know information on hospital- and clinic-based neurologic workup, diagnosis, and management. This thoroughly revised third edition puts key clinical information about a broad range of issues in neurology at your fingertips in seconds.  Contains up-to-date content in outline format, with bulleted lists, tables, and algorithms for quick reference.  Includes a new chapter on multiple sclerosis, expanded coverage of key topics such as lesion localization, neuroimaging, EEG, EMG.  Also, the book has chapters dedicated to all sub-specialties of neurology, including neuro immunological disorders, neurologic infectious disease, neuro-oncology. Many additional figures, tables, and references are new in this edition.  Each chapter progresses logically from neurologic signs and symptoms to differential diagnosis, workup and diagnosis, assessment of risks and benefits of available treatments, to treatment and prognosis.  Consult this high-yield handbook by clinical presentation, such as coma, stroke, headaches, and seizures, or by special topic, such as neurologic emergencies, neurocritical care, neuro-ophthalmology, behavioral neurology, and sleep medicine.  An invaluable tool for neurology residents as well as rotating psychiatry, PM&R, neurosurgery, medicine interns, medical students and others who are interested in neurology. Given content and easy access, serves as an excellent reference for ED, ICU, inpatient floors, clinic visits and teleneurology consults.   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.