An Interpretable Machine Learning Model for Predicting Early Neurological Deterioration Following Posttraumatic Acute Diffuse Brain Swelling.

Background: Diffuse brain swelling (DBS) significantly contributes to intracranial hypertension and poses a substantial risk of early neurological deterioration (END). This study aimed to develop and validate various machine learning (ML) models for predicting END in patients with traumatic DBS.

Methods: Clinical data were retrospectively collected from 208 consecutive adult patients diagnosed with traumatic DBS on admission (within 6 h after injury) at two centers.

An innovative emergency transportation scenario for mass casualty incident management: Lessons learnt from the Formosa Fun Color Dust explosion.

The purpose of this research is to analyze and introduce a new emergency medical service (EMS) transportation scenario, Emergency Medical Regulation Center (EMRC), which is a temporary premise for treating moderate and minor casualties, in the 2015 Formosa Fun Color Dust Party explosion in Taiwan. In this mass casualty incident (MCI), although all emergency medical responses and care can be considered as a golden model in such an MCI, some EMS plans and strategies should be estimated impartially to understand the truth of the successful outcome.Factors like on-scene triage, apparent prehospital time (appPHT), inhospital time (IHT), and diversion rate were evaluated for the appropriateness of the EMS transportation plan in such cases.