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NCT07407998
This prospective observational study aims to evaluate the performance of multiple artificial intelligence-based large language models in assigning American Society of Anesthesiologists Physical Status (ASA-PS) classifications in adult preoperative patients. AI-generated ASA scores obtained using both prompted and unprompted clinical scenario inputs will be compared with assessments performed by experienced anesthesiologists. The agreement, accuracy, readability, and overall quality of AI outputs will be analyzed to determine the potential role of artificial intelligence in supporting preoperative risk stratification.
NCT07364942
Preoperative evaluation is essential for identifying patient-related risks before elective surgery and for planning safe anesthesia management. Traditionally, this evaluation is performed by anesthesiologists based on clinical history, physical examination, comorbidities, and laboratory findings. This observational study aims to compare the clinical performance of a machine learning-based artificial intelligence system with anesthesiologist assessment during preoperative patient evaluation. The artificial intelligence system independently analyzes patient data and generates risk assessments, which are then compared with evaluations performed by anesthesiologists. The primary objective of the study is to assess the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk assessment. Secondary objectives include evaluating the accuracy and consistency of the artificial intelligence system and exploring its potential role as a decision-support tool in preoperative clinical practice. The findings of this study may contribute to understanding the potential benefits and limitations of artificial intelligence-assisted decision making in preoperative evaluation
NCT07100691
This is a multicentre, investigator-blinded, randomised controlled trial evaluating whether the use of oral metoclopramide before surgery can reduce the amount of residual gastric content in patients who are taking glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for weight loss. These medications are known to slow down gastric emptying, which may increase the risk of pulmonary aspiration during anaesthesia. Patients will be randomly assigned to either receive metoclopramide 24 hours before surgery or continue with standard care. The primary outcome will be the presence or absence of residual gastric content on ultrasound before surgery. Secondary outcomes include nausea, vomiting, constipation, and any adverse effects of the medication.