Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.

Background: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice.

Methods: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea.

Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events.

Background: Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance.

Methods: This is a retrospective multicenter cohort study including five tertiary-care academic children's hospitals.

Effects of predrilling on the osseointegration potential of mini-implants.

Objectives: To determine a reliable method of drilling a pilot hole when using a self-tapping surface-treated mini-implant and to evaluate stability after placement.

Materials And Methods: Implant sites were predrilled in 12 rabbits with two devices: a conventional motor-driven handpiece and a newly developed hand drill. Mini-implants were then inserted in a complete random block design.