Articles: emergency-medical-services.
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Magical thinking is a cognitive process characterized by beliefs in supernatural causality and the power of rituals. Grounded in personal convictions rather than objective reality, it involves subjective beliefs rather than magic tricks. Magical thinking's effects range from potentially positive, such as bringing hope and comfort, to negative consequences, including delays in seeking appropriate medical care and refusing evidence-based treatments. ⋯ For physicians and other EM professionals, addressing magical thinking requires cultural competence and empathetic engagement. Active listening and shared decision-making are essential to promote positive patient outcomes. By recognizing and understanding magical thinking and fostering effective communication, EPs can navigate the delicate balance of addressing patients' beliefs while delivering evidence-based care.
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British American Football (BAF) is a developing sport in the UK, with keen growth in the British Universities and Colleges Sport (BUCS) league. Participation in BAF carries risks and so to facilitate safe participation medical care services must be evaluated. ⋯ These findings provide key information on the status of medical provision, facilities and protocols in BUCS BAF. Data reveals a lack of consistent medical personnel, particularly at training and away games, and training in emergency care.
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A prehospital bypass strategy was suggested for large vessel occlusion. This study aimed to evaluate the effect of a bypass strategy using the gaze-face-arm-speech-time test (G-FAST) implemented in a metropolitan community. ⋯ The prehospital bypass strategy with G-FAST showed benefits for stroke patients.
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Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for ECG interpretation are limited in their accuracy, machine learning (ML) models have shown promise in several areas of clinical medicine. We performed a systematic review to compare the performance of ML-based ECG analysis to clinician or non-ML computerized ECG interpretation in the diagnosis of ACS for emergency department (ED) or prehospital patients. ⋯ ML models have overall higher discrimination and sensitivity but lower specificity than clinicians and non-ML software in ECG interpretation for the diagnosis of ACS. ML-based ECG interpretation could potentially serve a role as a "safety net", alerting emergency care providers to a missed acute MI when it has not been diagnosed. More rigorous primary research is needed to definitively demonstrate the ability of ML to outperform clinicians at ECG interpretation.