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https://www.selleckchem.com/products/fb23-2.html INTRODUCTION Precision medical practice emphasises early detection, improved surveillance and prevention through targeted intervention. Prediction models can help identify high-risk individuals to be targeted for healthy behavioural changes or medical treatment to prevent disease development and assist both health professionals and patients to make informed decisions. Concerns exist regarding the adequacy, accuracy, validity and reliability of prediction models. AIM The purpose of this study is to introduce readers to the basic concept of prediction modelling in precision health and recommend factors to consider before implementing a prediction model in clinical practice. METHODS Prediction models developed maintaining proper process and with quality prediction and validation can be used in clinical practice to improve patient care. RESULTS Aspects of prediction models that should be considered before implementation include appropriateness of the model for the intended purpose; adequacy of the model; validation, face validity and clinical impact studies of the model; a parsimonious model with data easily measured in clinical settings; and easily accessible models with decision support for successful implementation. DISCUSSION Choosing clinical prediction models requires cautious consideration and several practical factors before implementing a model in clinical practice.BackgroundUnrestricted access to direct-acting antiviral (DAA) therapy for hepatitis C virus (HCV) infection has been available in Australia since March 2016. Individuals with HIV-HCV co-infection are at a greater risk of liver fibrosis progression. This study estimated DAA treatment uptake among individuals with HIV-HCV co-infection, during the first year of DAA treatment access in Australia. Methods Pharmaceutical Benefits Scheme (PBS) data on dispensed DAA and antiretroviral therapy (ART) prescriptions from March 2016 to March 2017 were used for ana

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