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Case presentation: A 32-year-old man with a history of hemophilia A presented to the emergency department with right shoulder pain, swelling, and decreased range of motion.Discussion: Emergency physicians can use ultrasound to quickly and accurately identify hemarthrosis at the bedside.

BackgroundThe assessment and treatment of pediatric patients in the out-of-hospital environment often presents unique difficulties and stress for EMS practitioners.ObjectiveUse a mixed-methods approach to assess the current experience of EMS practitioners caring for critically ill and injured children, and the potential role of a simulation-based curriculum to improve pediatric prehospital skills.MethodsData were obtained from three sources in a single, urban EMS system: a retrospective review of local pediatric EMS encounters over one year; survey data of EMS practitioners' comfort with pediatric skills using a 7-point Likert scale; and qualitative data from focus groups with EMS practitioners assessing their experiences with pediatric patients and their preferred training modalities.Results2.1% of pediatric prehospital encounters were considered "critical," the highest acuity level. A total of 136 of approximately 858 prehospital providers responded to the quantitative survey; 34.4% of all respondents either somewhat disagree (16.4%), disagree (10.2%), or strongly disagree (7.8%) with the statement: "I feel comfortable taking care of a critically ill pediatric patient." Forty-seven providers participated in focus groups that resulted in twelve major themes under three domains. Specific themes included challenges in medication dosing, communication, and airway management. Participants expressed a desire for more repetition and reinforcement of these skills, and they were receptive to the use of high-fidelity simulation as a training modality.ConclusionsCritically ill pediatric prehospital encounters are rare. Over one third of EMS practitioners expressed a low comfort level in managing critically ill children. High-fidelity simulation may be an effective means to improve the comfort and skills of prehospital providers.

PurposeTo investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation.Materials and methodsHip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics.ResultsThe binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification.ConclusionA deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

Infectious flexor tenosynovitis (FTS) is a serious infection of the hand and wrist that can lead tonecrosis and amputation without prompt diagnosis and surgical debridement. Despite the growinguse of point-of-care ultrasound (POCUS) by emergency physicians there is only one reported caseof the use of POCUS for the diagnosis of infectious FTS in the emergency department setting.We present a case of a 58 year-old man where POCUS identified tissue necrosis and fluid alongthe flexor tendon sheath of the hand. Subsequent surgical pathology confirmed the diagnosis ofinfectious FTS. [West J Emerg Med. 2015;16(2):260–262.]

Clinical research has demonstrated the efficacy of closed-loop control of anesthesia using the bispectral index of the electroencephalogram as the controlled variable. These controllers have evolved to yield patient-specific anesthesia, which is associated with improved patient outcomes. Despite progress, the problem of patient-specific anesthesia remains unsolved. A variety of factors confound good control, including variations in human physiology, imperfect measures of drug effect, and delayed, hysteretic response to drug delivery. Reinforcement learning (RL) appears to be uniquely equipped to overcome these challenges; however, the literature offers no precedent for RL in anesthesia. To begin exploring the role RL might play in improving anesthetic care, we investigated the method's application in the delivery of patient-specific, propofol-induced hypnosis in human volunteers. When compared to performance metrics reported in the anesthesia literature, RL demonstrated patient-specific control marked by improved accuracy and stability. Furthermore, these results suggest that RL may be considered a viable alternative for solving other difficult closed-loop control problems in medicine. More rigorous clinical study, beyond the confines of controlled human volunteer studies, is needed to substantiate these findings. [ABSTRACT FROM AUTHOR]