Jaw fracture surgery or orthognathic surgery usually involves the application of an intermaxillary fixation (IMF). Obstructions that cannot be relieved by suction require an immediate release of IMF wires, but releasing the IMF may damage the surgical alignment of the facial bones. The mean time taken to release the jaws was an average of 2 minutes 9 seconds by hospital staff involved in caring for these patients. The aims of this study were to introduce a training model for wire-guided percutaneous cricothyrotomy in the patients applying an IMF and to perform the procedure for medical students. Our model consisted of a facial mannequin, a plastic breathing tube, 2 rolls of tapes, and a reservoir bag. The inner parts of the 2 used rolls of tape represent tracheal/cricoid rings (1-inch width for thyroid and half-inch width for cricoid), and the space between them represents the cricothyroid membrane, which is wrapped with Peha-Haft. A surgeon demonstrated the technique on the model, and then, 60 medical students who had never attended airway-training courses applied the Melker cricothyrotomy kit on the model. All 60 students completed the procedure successfully. The mean (SD) time needed to insert a cricothyrotomy catheter of the medical students was 175 (50) seconds (range, 76-297 s). Most of the students (54; 90%) performed it within 4 minutes; more than half (33; 55%), within 3 minutes. With our manikin model and Melker cricothyrotomy kit, 60 medical students completed the procedure successfully. This model can be useful to cricothyrotomy training for medical personnel.
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http://dx.doi.org/10.1097/SCS.0000000000000576 | DOI Listing |
The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.
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Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.
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