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 …

Objective To provide an overview of prediction models for risk of cardiovascular disease
(CVD) in the general population. Design Systematic review. Data sources Medline and …

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 …

Background Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor
neuron disease with a variable natural history. There are no accurate models that predict the …

Objectives It is widely acknowledged that the performance of diagnostic and prognostic
prediction models should be assessed in external validation studies with independent data …

Prognostic factors are associated with the risk of future health outcomes in individuals with a
particular health condition or some clinical start point (eg, a particular diagnosis). Research …

Access to big datasets from e-health records and individual participant data (IPD) meta-
analysis is signalling a new advent of external validation studies for clinical prediction …

Validation of prediction models is highly recommended and increasingly common in the
literature. A systematic review of validation studies is therefore helpful, with meta-analysis …

Individual participant data (IPD) meta‐analysis is an increasingly used approach for
synthesizing and investigating treatment effect estimates. Over the past few years, numerous …

In prediction model research, external validation is needed to examine an existing model's
performance using data independent to that for model development. Current external …