Poisoning and Drug Overdose 8E 與 H-Module 使用情況與統計

When every moment counts, count on Poisoning & Drug Overdose A Doody's Core Title for 2022! Speed is crucial when dealing with toxicologic and drug-related emergencies. Finding answers quickly is easier than ever with this streamlined eighth edition of Poisoning and Drug Overdose. This instant-answer guide provides the critical information needed to diagnose and manage drug-related emergencies and chemical exposures. Updated with newly released drugs and new information on existing drugs, the guide covers initial emergency management, including treatment of coma, seizures and hypotension; physical and laboratory diagnosis; and methods of decontamination and enhanced elimination of poisons. Poisoning and Drug Overdose, Eighth Edition is divided into four sections: • Section I. Provides a stepwise approach to the evaluation and treatment of coma, seizures, shock, and other complications of poisoning and the proper use of gastric decontamination and dialysis procedures. • Section II. Lists specific poisons and drugs, as well as the pathophysiology, toxic dose and level, clinical presentation, diagnosis, and specific treatment associated with each substance. • Section III. Covers descriptions of therapeutic drugs and antidotes, including pharmacology indications, adverse effects, drug interactions, and recommended dosage. • Section IV. Describes the approach to hazardous materials incidents; the evaluation of occupational exposures; and the toxic effects, physical properties, and workplace limits for over 500 common industrial chemicals. Poisoning and Drug Overdose, Eighth Edition is enhanced by numerous tables and charts, as well as a user-friendly index. This trusted resource has consistently been relied upon by front line professionals responding to drug-related emergencies and chemical exposures. This app is very intuitive and easy to navigate, allowing you to browse the contents or search for topics. The powerful search tool gives you word suggestions that appear in the text as you type, so it is lightning fast and helps with spelling those long medical terms. The search tool also keeps a recent history of past search terms so you can go back to a previous search result very easily. You have the ability to create notes and bookmarks separately for text and images to enhance your learning. You can also change the text size for easier reading. After the app has been downloaded, no internet connection is needed to retrieve the content of the app. All of the text and images are available to you on your device anytime, anywhere, and lightning fast. This app is also automatically optimized for whatever size device you are currently using, either phone or tablet. This interactive app contains the full content of Poisoning and Drug Overdose, Eighth Edition by McGraw-Hill Education. ISBN-10: 1264259085 ISBN-13: 978-1264259083 Editors: Kent R. Olson, MD, FACEP, FACMT, FAACT Craig G. Smollin, MD, FACMT Associate Editors: Ilene B. Anderson, PharmD Susan Y. Kim-Katz, PharmD Neal L. Benowitz, MD Justin C. Lewis, PharmD, DABAT Paul D. Blanc, MD, MSPH Alan H. B. Wu, PhD Disclaimer: This app is intended for the education of healthcare professionals and not as a diagnostic and treatment reference for the general population. Please seek your medical professionals advice before making medical decisions. Developed by Usatine Media Richard P. Usatine, MD, Co-President, Professor of Family & Community Medicine, Professor of Dermatology and Cutaneous Surgery, University of Texas Health San Antonio Peter Erickson, Co-President, Lead Software Developer

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H-Module

Disclaimer The H-Module is a medical supporting tool used for educational purposes of the haematological acute radiation syndrome (H-ARS) only. Before making any medical decisions based on H-Module results, clinicians specialized in hemato-oncology and experienced in H-ARS should be consulted. The Threat During radiological (e.g. terrorist attack) or nuclear events (e.g. nuclear power plant accidents or use of an improvised nuclear device) subjects will be exposed to ionizing radiation. With a delay of days or weeks after radiation, injured patients will become very sick, requiring an early hospitalization and intensive care in order to survive. The Aim Physicians require rapid guidance for early and high-throughput diagnosis and therapeutic interventions of the H-ARS. Within the first three days after exposure and prior to the onset of the disease manifestation this App allows to: (1) Identify the worried well (H0) to avoid misdirection of limited clinical resources, (2) identify individuals, who will require hospitalization and if applicable intensive care (H2-4 H-ARS), (3) Identify exposed individuals, who will develop a severe/lethal degree of the hematopoietic syndrome (H3-4 H-ARS). Depending on the changes in blood cell counts, no precise allocation to a certain H-ARS severity category can be provided. In this case, a severity range will be shown and associated likelihoods of the prediction (given as positive and negative predictive values) calculated. The Tool We focused on groups of clinical significance and used logistic regression analysis to achieve a discrimination between these groups during the first three days after exposure: 1. H0 vs H1-4, identification of unexposed individuals (H0) 2 .H0-1 vs H2-4, identification of individuals requiring hospitalization (H2-4) 3 .H0-2 vs H3-4, identification of individuals who will develop a severe/lethal degree of the H-ARS (H3-4). For each of these group comparisons we examined how well changes in lymphocytes, granulocytes and thrombocytes contributed to their discrimination and build corresponding mathematical models for each day. For days 2 and 3 we examined which blood cell counts from that same day or which combination of blood cell counts from previous days (sequential diagnosis) might provide the best model for discriminating the three binary categories examined (table 1). Depending on the day and the binary category one out of these 21 models will be activated by the App. Diagnostic and therapeutic recommendations from these models are finally aggregated following an algorithm as stated elsewhere (Majewski et al. 2020). The likelihood (positive or negative predictive value) in favor of the higher or lower binary category are reflected in percent.