British journal of anaesthesia
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Randomized Controlled Trial
Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.
Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment. ⋯ NCT05042804.
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The issue of potentially harmful effects of neurotoxicity or anaesthesia management on children undergoing general anaesthesia is still not resolved. Studies have so far been limited by methodological problems. In a retrospective cohort study, a new noninvasive method was used to demonstrate visual processing changes in children with a single previous exposure to anaesthesia. We need new noninvasive methods that can be used before and after exposure to anaesthesia and surgery to detemine possible effects on long-term neurodevelopment.
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Preoperative risk prediction is an important component of perioperative medicine. Machine learning is a powerful tool that could lead to increasingly complex risk prediction models with improved predictive performance. Careful consideration is required to guide the machine learning approach to ensure appropriate decisions are made with regard to what we are trying to predict, when we are trying to predict it, and what we seek to do with the results.
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There is a lack of qualitative data on the negative effects of workplace stressors on the well-being of healthcare professionals in hospitals in Africa. It is unclear how well research methods developed for high-income country contexts apply to different cultural, social, and economic contexts in the global south. ⋯ The Rwandan healthcare system presents many challenges which can become profoundly stressful for the workforce. Consideration of reduced personal and collective accomplishment, of moral injury, and its diverse downstream effects on the whole healthcare system may better represent the costs of burnout Rwanda. It is likely that improving the causes of work-based stress will require a significant investment in improving staffing and working conditions.