Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

Latent class models (LCMs) combine the results of multiple diagnostic tests through a
statistical model to obtain estimates of disease prevalence and diagnostic test accuracy in …

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or
prognosis in healthcare. Hundreds of prediction models are published in the medical …

Background The assessment of calibration performance of risk prediction models based on
regression or more flexible machine learning algorithms receives little attention. Main text …

Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …

Binary logistic regression is one of the most frequently applied statistical approaches for
developing clinical prediction models. Developers of such models often rely on an Events …

Background Ten events per variable (EPV) is a widely advocated minimal criterion for
sample size considerations in logistic regression analysis. Of three previous simulation …

Background Prognostic models to predict the risk of clinical deterioration in acute COVID-19
cases are urgently required to inform clinical management decisions. Methods We …

The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is
growing rapidly, but it is unknown whether any are suitable for widespread clinical …