Aim: Early defibrillation should achieve the highest survival rates when applied within the first minutes after the collapse. Public access defibrillation programs have increased the population awareness of the importance of defibrillation. Schoolchildren should be trained in basic life support (BLS) skills and some countries have included BLS in their school syllabus. However, little is known of the current knowledge and ability of schoolchildren to use an automated external defibrillator (AED).
Methods: A multicentric descriptive study, 1295 children from 6 to 16 years of age without previous BLS or AED training. Subjects performed a simulation with an AED and a manikin with no training or feedback and were evaluated by means of a checklist.
Results: A total of 258 participants (19.9%) were able to simulate an effective and safe defibrillation in less than 3min and 52 (20.1% of this group) performed it successfully. A significant correlation between objective and age group was observed (G=0.172) (p<0.001). The average time to deliver a shock was 83.3±26.4s; that time decreased significantly with age [6 YO (108.3±40.4) vs. 16 YO (64.7±18.6)s] (p<0.001).
Conclusions: Around 20% of schoolchildren without prior training are able to use an AED correctly in less than 3min following the device's acoustic and visual instructions. However, only one-fifth of those who showed success managed to complete the procedure satisfactorily. These facts should be considered in order to provide a more accurate definition and effective implementation of BLS/AED teaching and training at schools.
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http://dx.doi.org/10.1016/j.resuscitation.2016.06.007 | DOI Listing |
J Am Mosq Control Assoc
January 2025
Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, 30602.
Accurate enumeration of mosquito eggs is crucial for various entomologic studies, including investigations into mosquito fecundity, life history traits, and vector control strategies. Traditional manual counting methods are labor intensive and prone to human error, highlighting the need for automated systems. This study presents a stand-alone automated mosquito egg counting system using a Raspberry Pi computer, high-quality camera, light-emitting diode ring light source, and a Python script leveraging the Open Source Computer Vision library.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Medical Informatics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan.
Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in patient follow-up and treatment. To address this, we developed a rule-based natural language processing (NLP) algorithm to detect cancer-suspicious findings from Japanese radiology reports. The dataset used consisted of chest and abdomen CT reports from six institutions.
View Article and Find Full Text PDFEBioMedicine
January 2025
CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.
Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.
Sci Robot
January 2025
Research Center for Information and Communication Technologies, Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain.
Robots have to adjust their motor behavior to changing environments and variable task requirements to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective.
View Article and Find Full Text PDFErgonomics
January 2025
Human Factors Research Group, University of Nottingham, University Park, Nottingham, United Kingdom.
In a novel, on-road study, using a 'Ghost Driver' to emulate an automated vehicle (AV), we captured over 10 hours of video (n = 520) and 64 survey responses documenting the behaviour and attitudes of pedestrians in response to the AV. Three prototype external human-machine interfaces (eHMIs) described the AV's behaviour, awareness and intention using elements of anthropomorphism: High (human face), Low (car motif), Abstract (partial representation of human features that lacked precise visual reference); these were evaluated against a (no eHMI) baseline. Despite many pedestrians reporting that they still relied on vehicular cues to negotiate their crossing, there was a desire/expectation expressed for explicit communication with future AVs.
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