Journal of clinical anesthesia
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Multicenter Study
Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction.
This study investigates the potential of ChatGPT-4, developed by OpenAI, in enhancing medical decision-making processes, particularly in preoperative assessments using the American Society of Anesthesiologists (ASA) scoring system. The ASA score, a critical tool in evaluating patients' health status and anesthesia risks before surgery, categorizes patients from I to VI based on their overall health and risk factors. Despite its widespread use, determining accurate ASA scores remains a subjective process that may benefit from AI-supported assessments. This research aims to evaluate ChatGPT-4's capability to predict ASA scores accurately compared to expert anesthesiologists' assessments. ⋯ The findings suggest that ChatGPT-4 can significantly contribute to the medical field by supporting anesthesiologists in preoperative assessments. This study not only demonstrates ChatGPT-4's efficacy in medical data analysis and decision-making but also opens new avenues for AI applications in healthcare, particularly in enhancing patient safety and optimizing surgical outcomes. Further research is needed to refine AI models for complex case assessments and integrate them seamlessly into clinical workflows.
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Three linked clinical observations prompted our current understanding of perioperative heat balance. The first was the extraordinarily rapid decrease in core temperature after induction of general anesthesia which led to an understanding of redistribution hypothermia. The second was the linear reduction in core temperature during the subsequent 2-3 h which led to an understanding of anesthetic effects on metabolic heat production and factors that influence cutaneous heat loss. And the third was the observation that core temperature reaches a plateau at about 34.5 °C which led to the understanding that thermoregulatory vasoconstriction re-emerges when patients become sufficiently hypothermic, and that arteri-venous shunt constriction constrains metabolic heat to the core thermal compartment.
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Periodic fever syndromes are autoinflammatory disorders associated with recurrent fevers unrelated to infection. Little is known about the perioperative management of patients with these syndromes, and existing literature consists primarily of case reports and occasional case series. This narrative review discusses background information and diagnostic criteria for the three most common periodic fever syndromes: periodic fever, aphthous stomatitis, pharyngitis, adenitis (PFAPA), familial Mediterranean fever (FMF), and TNF receptor-associated periodic syndrome (TRAPS), and describes perioperative considerations for anesthesia providers when caring for the patient with a periodic fever syndrome. We include a systems-based framework in which to organize these considerations.
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Randomized Controlled Trial Comparative Study
Erector spinae plane block versus quadratus lumborum block for postoperative analgesia after laparoscopic nephrectomy: A randomized controlled trial.
We compared the analgesic effects of erector spinae plane block versus quadratus lumborum block following laparoscopic nephrectomy. ⋯ Compared with quadratus lumborum block, erector spinae plane block provided better analgesia as manifested by lower opioid consumption and pain intensity for up to 24 h after laparoscopic nephrectomy.
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Observational Study
Development and validation of a machine learning model to predict postoperative delirium using a nationwide database: A retrospective, observational study.
Postoperative delirium is a neuropsychological syndrome that typically occurs in surgical patients. Its onset can lead to prolonged hospitalization as well as increased morbidity and mortality. Therefore, it is important to promptly identify its signs. This study aimed to develop and validate a machine learning predictive model for postoperative delirium using extensive population data. ⋯ Using extensive Diagnostic Procedure Combination data, we successfully created and validated a machine learning model for predicting postoperative delirium. This model may facilitate prediction of postoperative delirium.