Numerous risk prediction models are available for predicting delirium after cardiac surgery,
but few have been directly compared with one another or been validated in an independent …

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy
of the estimated probabilities. Unfortunately, such evaluation is problematic in large …

Background: Risk scores can be useful in clinical risk stratification and accurate allocations
of medical resources, helping health providers improve patient care. Point-based scores are …

Our aim was to create simple and largely scalable machine learning-based algorithms that
could predict mortality in a real-time fashion during intensive care after traumatic brain injury …

Background Despite the central role of nurses in intensive care, a relationship between
intensive care nurse workload/staffing ratios and survival has not been clearly established …

Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …

Background Despite the existence of numerous published models predicting the risk of
caesarean delivery in women undergoing induction of labour (IOL), validated models are …

A model to classify the difficulty of videolaryngoscopic tracheal intubation has yet to be
established. The videolaryngoscopic intubation and difficult airway classification (VIDIAC) …

The calibration belt is a graphical approach designed to evaluate the goodness of fit of
binary outcome models such as logistic regression models. The calibration belt examines …

We evaluated the performance of cardiac arrest-specific prognostication scores developed
for outcome prediction in the early hours after out-of-hospital cardiac arrest (OHCA) in …