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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
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Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study

An external validation study evaluates the performance of a prediction model in new data, but many of these studies are too small to provide reliable answers. In the third article of their series on model evaluation, Riley and colleagues describe how to calculate the sample size required for external validation studies, and propose to avoid rules of thumb by tailoring calculations to the model and setting at hand.

External validation studies evaluate the performance of one or more prediction models (eg, developed previously using statistical, machine learning, or artificial intelligence approaches) in a different dataset to that used in the model development process.1 2 3 Part 2 in our series describes how to undertake a high quality external validation study,4 including the need to estimate model performance measures such as calibration (agreement between observed and predicted values), discrimination (separation between predicted values in those with and without an outcome eve ....

United Kingdom , City Of , Garys Collins , Lucinda Archer , Kym Ie Snell , Ben Van Calster , Thomas Pa Debray , Van Calster , Richardd Riley , Maarten Van Smeden , Care Research , Cancer Research United Kingdom , Research Foundation Flanders , Birmingham Biomedical Research Centre , Better Methods Research , Sciences Research Council , University Of Birmingham , University Hospitals Birmingham , Department Of Health , Research Council , National Institute For Health , Transparent Reporting , Individual Prognosis , Physical Sciences Research Council , Medical Research Council , National Institute ,

Evaluation of clinical prediction models (part 1): from development to external validation

Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.

Healthcare decisions for individuals are routinely made on the basis of risk or probability.1 Whether this probability is that a specific outcome or disease is present (diagnostic) or that a specific outcome will occur in the future (prognostic), it is important to know how these probabilities are estimated and whether they are accurate. Clinical prediction models estimate outcome risk for an individual conditional on their characteristics of multiple predictors (eg, age, family history, symptoms, blood pressure). Examples include the ISARIC (International ....

Glasgow City , United Kingdom , Administrative Coordinating Center , National Institutes Of Health , Care Research Birmingham Biomedical Centre , Research Foundation , National Heart , National Institute For Health , University Of Birmingham , Infection Consortium , Cancer Research United Kingdom , Research Council Better Methods , Sciences Research Council , International Severe Acute , Translational Sciences Clinical Science Award , Research Council , Blood Institute , National Center , University Hospitals Birmingham , International Severe Acute Respiratory , Emerging Infection Consortium , Transparent Reporting , Individual Prognosis Or Diagnosis , National Institute , Care Excellence , Physical Sciences Research Council ,

Okaloosa County middle school bookkeeper under investigation for misappropriating funds

Okaloosa County middle school bookkeeper under investigation for misappropriating funds
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Max Bruner Jr , Heather Williams , Briggs Ingram , April Briggs , Okaloosa County School District , Okaloosa County School Board , Okaloosa County , Middle School , Internal Fund , Chief Financial Officer , Internal Funds , County Superintendent Marcus Chambers , Superintendent Chambers ,