Anaesthetic considerations and challenges during awake craniotomy.

ABSTRACT: This article summarises the key anaesthetic considerations and challenges surrounding the perioperative management of a patient undergoing awake craniotomy. The main goals include patient comfort, facilitation of patient cooperation during the critical awake phase and maintenance of optimal operating conditions. These are achieved through appropriate patient selection and preparation, familiarity with the complexity of each surgical phase and potential complications that may arise, as well as maintenance of close communication among all team members. Challenges such as loss of patient cooperation, loss of airway, intraoperative nausea and vomiting, seizures, cerebral oedema, hypertension, blood loss and use of intraoperative magnetic resonance imaging are discussed. The importance of teamwork, competence, vigilance and clear management strategies for potential complications to maximise patient outcomes is also highlighted.

Improving compliance with personal protective equipment among anaesthetists through behaviour-changing interventions during the coronavirus disease 2019(COVID-19) pandemic.

Since 2020, the coronavirus disease 2019 (COVID-19) pandemic has seen many changes in our daily infection prevention behaviours so as to reduce healthcare-associated transmission of COVID-19 in patients and healthcare workers. In the early phases of the COVID-19 pandemic of 2020, there was much emphasis on compliant personal protective equipment utilization in the operating theatres (OTs) for COVID-19-positive patients. However, during this period, there was a lack of international validated protocols on the appropriate handling and subsequent storage of personal protective equipment in the context of aerosol-generating procedures in OTs for asymptomatic antigen rapid test (ART)-negative patients. Given the potential for transmission even with a negative ART due to the incubation period, our team developed a checklist of eye protection (e.g. goggles/face shield) and N95 mask handling and storage in non-isolation OTs for these patients. We sought to improve the compliance of best practices from 20% to 80% amongst junior anaesthetists in Singapore General Hospital so as to prevent infection transmission and cross-contamination in the OT through education and behaviour-changing interventions. This quality improvement project took place over 19 weeks from June to October 2020 by our team of anaesthetists and nurse clinicians in the non-isolation OT setting. To analyse the problem, we performed a root cause analysis to understand attitudes and beliefs driving their behaviour. The top 80% of cited root causes for non-compliance then guided prioritization of resources for subsequent behaviour-changing interventions. Using the comprehensive infection control checklist, we conducted several plan-do-study-act cycles while implementing this new checklist amongst junior anaesthetists. A total of 227 assessments of junior anaesthetists were made in the care of asymptomatic ART-negative patients. Compliance with correctly handling goggles post-intubation started out as 33.3% in Week 1, which improved to 78.5% by Week 19. Compliance with goggle storage and face shield disposal started out at 13.6% in Week 1 and increased to 78.6% by Week 19. We attributed this improvement to education and behaviour-changing interventions. This quality improvement project focusing on improving compliance with personal protective equipment utilization during the COVID-19 pandemic in the management of asymptomatic ART-negative patients in non-isolation OTs demonstrated the importance of interventions of education, persuasion, modelling, and training in effecting and sustaining organizational behaviour change in physicians and other healthcare personnel.

Infection Control for Perioperative Ultrasonography and Echocardiography.

Ultrasound technology has revolutionized point-of-care diagnostics, decision-making, and the guidance of interventional procedures in Anesthesiology and Perioperative Medicine. Recent literature has highlighted important infection control considerations when performing transesophageal or transthoracic echocardiography, point-of-care ultrasound, and ultrasound-guided procedures. This narrative review focuses on operator precautions and disinfection methods and summarizes key recommendations from the international Echocardiography and Radiology Societies.

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.

We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute of Technology (MIT)'s largest mathematics courses (Single Variable and Multivariable Calculus, Differential Equations, Introduction to Probability and Statistics, Linear Algebra, and Mathematics for Computer Science) and Columbia University's Computational Linear Algebra. We solve questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems designed to assess mathematical reasoning. We randomly sample questions and generate solutions with multiple modalities, including numbers, equations, and plots. The latest GPT-3 language model pretrained on text automatically solves only 18.8% of these university questions using zero-shot learning and 30.8% using few-shot learning and the most recent chain of thought prompting. In contrast, program synthesis with few-shot learning using Codex fine-tuned on code generates programs that automatically solve 81% of these questions. Our approach improves the previous state-of-the-art automatic solution accuracy on the benchmark topics from 8.8 to 81.1%. We perform a survey to evaluate the quality and difficulty of generated questions. This work automatically solves university-level mathematics course questions at a human level and explains and generates university-level mathematics course questions at scale, a milestone for higher education.