Evaluation of machine learning models as decision aids for anesthesiologists.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists.

Circumplex Model of Affect: A Measure of Pleasure and Arousal During Virtual Reality Distraction Analgesia.

Objective: Immersive virtual reality (VR) distraction provides clinically effective pain relief and increases subjective reports of "fun" in medical settings of procedural pain. The goal of this study was to better describe the variable of "fun" associated with VR distraction analgesia using the circumplex model (pleasure/arousal) of affect.

Materials And Methods: Seventy-four healthy volunteers (mean age, 29 years; 37 females) received a standardized, 18-minute, multimodal pain sequence (alternating thermal heat and electrical stimulation to distal extremities) while receiving immersive, interactive VR distraction.