The performance of a predictive model is overestimated when simply determined on the sample
of subjects that was used to construct the model. Several internal validation methods are …

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 in …

Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or
an event in the future course of disease (prognosis) for individual patients. Although …

Objectives The objective of this study was to compare performance of logistic regression (LR)
with machine learning (ML) for clinical prediction modeling in the literature. Study Design …

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the …

Logistic regression analysis may well be used to develop a prognostic model for a
dichotomous outcome. Especially when limited data are available, it is difficult to determine an …

A concerted effort to tackle the global health problem posed by traumatic brain injury (TBI) is
long overdue. TBI is a public health challenge of vast, but insufficiently recognised, …

Objective Calibrated risk models are vital for valid decision support. We define four levels of
calibration and describe implications for model development and external validation of …

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

Previous studies have indicated that there is no consensus about management of mild
traumatic brain injury (mTBI) at the emergency department (ED) and during hospital admission. …