Clinical Prediction Rules: A Physical Therapy Reference Manual, is intended to be used for multiple musculoskeletal courses. It includes musculoskeletal clinical prediction rules organized by region, thus allowing for its repeated use during the upper and lower quarter as well as in the students spine coursework. Additionally this manual includes multiple medical screening prediction rules, making it appropriate for differential diagnosis and diagnostic imaging coursework. Perfect for entry-level physical therapy programs, this text is also suitable for post-professional physical therapy programs, especially those that include an orthopaedic residency or manual therapy fellowship program, and as a reference manual for students going out on their clinical rotations.
"Clinical versus Statistical Prediction" is Paul Meehl's famous examination of benefits and disutilities related to the different ways of combining information to make predictions. It is a clarifying analysis as relevant today as when it first appeared. A major methodological problem for clinical psychology concerns the relation between clinical and actuarial methods of arriving at diagnoses and predicting behavior. Without prejudging the question as to whether these methods are fundamentally different, we can at least set forth the obvious distinctions between them in practical applications. The problem is to predict how a person is going to behave: What is the most accurate way to go about this task? "Clinical versus Statistical Prediction" offers a penetrating and thorough look at the pros and cons of human judgment versus actuarial integration of information as applied to the prediction problem. Widely considered the leading text on the subject, Paul Meehl's landmark analysis is reprinted here in its entirety, including his updated preface written forty-two years after the first publication of the book. This classic work is a must-have for students and practitioners interested in better understanding human behavior, for anyone wanting to make the most accurate decisions from all sorts of data, and for those interested in the ethics and intricacies of prediction. As Meehl puts it, " "When one is dealing with human lives and life opportunities, it is immoral to adopt a mode of decision-making which has been demonstrated repeatedly to be either inferior in success rate or, when equal, costlier to the client or the taxpayer.""
Clinical reasoning is a key skill underpinning clinical expertise. Clinical Reasoning in Musculoskeletal Practice is essential reading for the musculoskeletal practitioner to gain the contemporary knowledge and thinking capacity necessary to advance their reasoning skills. Now in its 2nd edition, it is the only all-in-one volume of up-to-date clinical reasoning knowledge with real-world case examples illustrating expert clinical reasoning. This new edition includes: • Comprehensively updated material and brand new chapters on pain science, psychosocial factors, and clinical prediction rules. • The latest clinical reasoning theory and practical strategies for learning and facilitating clinical reasoning skills. • Cutting-edge pain research and relevant psychosocial clinical considerations made accessible for the musculoskeletal practitioner. • The role of clinical prediction rules in musculoskeletal clinical reasoning. • 25 all new real-world, clinical cases by internationally renowned expert clinicians allowing you to compare your reasoning to that of the best.
"What is going to happen to me?" Most patients ask this question during a clinical encounter with a health professional. As well as learning what problem they have (diagnosis) and what needs to be done about it (treatment), patients want to know about their future health and wellbeing (prognosis). Prognosis research can provide answers to this question and satisfy the need for individuals to understand the possible outcomes of their condition, with and without treatment. Central to modern medical practise, the topic of prognosis is the basis of decision making in healthcare and policy development. It translates basic and clinical science into practical care for patients and populations. Prognosis Research in Healthcare: Concepts, Methods and Impact provides a comprehensive overview of the field of prognosis and prognosis research and gives a global perspective on how prognosis research and prognostic information can improve the outcomes of healthcare. It details how to design, carry out, analyse and report prognosis studies, and how prognostic information can be the basis for tailored, personalised healthcare. In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health. Prognosis Research in Healthcare: Concepts, Methods and Impact, addresses all types of prognosis research and provides a practical step-by-step guide to undertaking and interpreting prognosis research studies, ideal for medical students, health researchers, healthcare professionals and methodologists, as well as for guideline and policy makers in healthcare wishing to learn more about the field of prognosis.
Fully-updated edition of this award-winning textbook, arranged by presenting complaints with full-color images throughout. For students, residents, and emergency physicians.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Technology assessment can lead to the rapid application of essential diagnostic technologies and prevent the wide diffusion of marginally useful methods. In both of these ways, it can increase quality of care and decrease the cost of health care. This comprehensive monograph carefully explores methods of and barriers to diagnostic technology assessment and describes both the rationale and the guidelines for meaningful evaluation. While proposing a multi-institutional approach, it emphasizes some of the problems involved and defines a mechanism for improving the evaluation and use of medical technology and essential resources needed to enhance patient care.
The “essential” companion to the landmark Users' Guides to the Medical Literature - completely revised and updated! 5 STAR DOODY'S REVIEW! "This second edition is even better than the original. Information is easier to find and the additional resources that will be available at www.JAMAevidence.com will provide readers with a one-stop source for evidence-based medicine."--Doody's Review Service Evidence-based medicine involves the careful interpretation of medical studies and its clinical application. And no resource helps you do it better-and faster-than Users' Guides to the Medical Literature: Essentials of Evidence-Based Clinical Practice. This streamlined reference distills the most clinically-relevant coverage from the parent Users' Guide Manual into one highly-focused, portable resource. Praised for its clear explanations of detailed statistical and mathematical principles, The Essentials concisely covers all the basic concepts of evidence-based medicine--everything you need to deliver optimal patient care. It's a perfect at-a-glance source for busy clinicians and students, helping you distinguish between solid medical evidence and poor medical evidence, tailor evidence-based medicine for each patient, and much more. Now in its second edition, this carry-along quick reference is more clinically relevant--and more essential--than ever! FEATURES Completely revised and updated with all new coverage of the basic issues in evidence-based medicine in patient care Abundant real-world examples drawn from the medical literature are woven throughout, and include important related principles and pitfalls in using clinical research in patient care decisions Edited by over 60 internationally recognized editors and contributors from around the globe Also look for JAMAevidence.com, a new interactive database for the best practice of evidence based medicine.
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
