Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner’s capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders’ expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train different learners regardless of their existing skills, expertise, and ability to handle cognitive load. To address this problem, we propose an end-to-end framework for a trauma simulation that actively classifies a participant's level of cognitive load and expertise for the development of a dynamically adaptive simulation. To facilitate this solution, trauma simulations were developed for the collection of electrocardiogram (ECG) signals of both novice and expert practitioners. A multitask deep neural network was developed to utilize this data and classify high and low cognitive load, as well as expert and novice participants. A leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness of our model, achieving an accuracy of 89.4% and 96.6% for classification of cognitive load and expertise, respectively.

We present a novel multimodal dataset for Cognitive Load Assessment in REal-time (CLARE). The dataset contains physiological and Gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset includes four modalities: Electrocardiography (ECG), Electrodermal Activity (EDA), Electroencephalogram (EEG), and Gaze tracking. Each participant completed four nine-minute sessions using the MATB-II software, a computer-based mental workload task. The sessions were divided into one-minute segments of varying complexity to induce different levels of cognitive load. During the experiment, participants reported their cognitive load every 10 seconds. For the dataset, we also provide benchmark binary classification results with machine learning and deep learning models on two different evaluation schemes, namely, 10-fold and leave-one-subject-out (LOSO) cross-validation. Benchmark results show that for 10-fold evaluation, the Transformer based deep learning model achieves the best classification performance with ECG, EDA, and Gaze. In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.

Objectives: Cannabis hyperemesis syndrome is characterized by bouts of protracted vomiting in regular users of cannabis. We wondered whether this poorly understood condition is idiosyncratic, like motion sickness or hyperemesis gravidarum, or the predictable dose-response effect of prolonged heavy use.Methods: Adults with an emergency department visit diagnosed as cannabis hyperemesis syndrome, near-daily use of cannabis for ≥6 months, and ≥2 episodes of severe vomiting in the previous year were age- and sex-matched to two control groups: RU controls (recreational users without vomiting), and ED controls (patients in the emergency department for an unrelated condition). Δ⁹-Tetrahydrocannabinol (THC), cannabinol (CBN), cannabidiol, and 11-nor-9-carboxy-THC concentrations in scalp hair were compared for subjects with positive urine THC.Results: We obtained satisfactory hair samples from 46 subjects with positive urine THC: 16 cases (age 26.8 ± 9.2 years; 69% male), 16 RU controls and 14 ED controls. Hair cannabinoid concentrations were similar between all three groups (e.g. cases THC 220 [median; IQR 100,730] pg/mg hair, RU controls 150 [71,320] and ED controls 270 [120,560]). Only the THC:CBN ratio was different between groups, with a 2.6-fold (95%CI 1.3,5.7) lower age- and sex-adjusted ratio in cases than RU controls. Hair cannabidiol concentrations were often unquantifiably low in all subjects.Conclusions: Similar hair cannabinoid concentrations in recreational users with and without hyperemesis suggest that heavy use is necessary but not sufficient for hyperemesis cannabis. Our results underline the high prevalence of chronic heavy cannabis use in emergency department patients and our limited understanding of this plant's adverse effects.

We describe a case of chemical pneumonitis with alveolar hemorrhage after acute exposure to a common household pyrethrin spray in a patient on anticoagulation with underlying chronic obstructive pulmonary disease (COPD). We review the relevant literature pertaining to adverse respiratory effects of pyrethrin based insecticide spray exposures. This case report highlights the importance of including potential inhaled household respiratory irritants in the differential for patients that present to the emergency department (ED) in respiratory distress. While aerosolized pyrethroid and pyrethrin containing domestic products generally do not cause significant respiratory symptoms when accidentally inhaled, the risk of serious adverse events may be increased in those patients with preexisting respiratory disease or who have used the product within a confined space. Timely recognition of adverse respiratory events related to pyrethroid or pyrethrin inhalation, airway protection and supportive care are the pillars of treatment for these patients.