Performance of a Large Language Model on Japanese Emergency Medicine Board Certification Examinations.

Background: Emergency physicians need a broad range of knowledge and skills to address critical medical, traumatic, and environmental conditions. Artificial intelligence (AI), including large language models (LLMs), has potential applications in healthcare settings; however, the performance of LLMs in emergency medicine remains unclear.

Methods: To evaluate the reliability of information provided by ChatGPT, an LLM was given the questions set by the Japanese Association of Acute Medicine in its board certification examinations over a period of 5 years (2018-2022) and programmed to answer them twice.

Ventilator management and risk of air leak syndrome in patients with SARS-CoV-2 pneumonia: a single-center, retrospective, observational study.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia is reportedly associated with air leak syndrome (ALS), including mediastinal emphysema and pneumothorax, and has a high mortality rate. In this study, we compared values obtained every minute from ventilators to clarify the relationship between ventilator management and risk of developing ALS.

Methods: This single-center, retrospective, observational study was conducted at a tertiary care hospital in Tokyo, Japan, over a 21-month period.

Cerebral edema associated with diabetic ketoacidosis: Two case reports.

Background: Diabetic ketoacidosis (DKA) is associated with a high mortality rate, especially if cerebral edema develops during the disease course. It is rarer and more severe in adults than in children. We present cases of two patients with cerebral edema-related DKA.

Timing and order of surgeries for thoracic trauma with multiple injuries: A case report.

The timing and order of multiple surgeries for patients with multiple thoracic injuries have not been standardized. A 75-year-old man, who was injured because of a closing elevator door, underwent intubation, bilateral chest drain insertion, and massive blood transfusion due to shock and respiratory distress. Computed tomography showed hemopneumothorax with extravasation, tracheobronchial injury, aortic injury, thoracic vertebral anterior dislocation, and multiple rib fractures.

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19.

Background: The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19.

Methods: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission.