We observed better postoperative pain control in children who received IT vs IV opioids for the first 16 h with no discernible difference thereafter. The intrathecal group experienced higher incidences of pruritus, constipation, and hypotension.

Background Despite the advantages of simulation-based training, trainees are typically unable to view internal anatomical structures. This limitation can be overcome by using mixed reality (MR) wherein 3-D virtual anatomical images can be projected. This study was designed to evaluate the efficacy of an MR trainer for peripheral intravenous catheter (PIVC) placement. Methods Sixty-two participants used projected images of arm veins to place a PIVC in a mannequin arm. Participants were evaluated using a checklist on their ability to successfully place the PIVC. Participants completed a survey to elicit demographic information and perceptions of the trainer. A follow-up survey at two-weeks assessed clinical experiences with PIVC placement since using the MR trainer. Results First attempt catheter placement was successful in 48 (77.4%) cases. Only 11 (17.7%) and 3 (4.8%) of participants caused ‘extravasation’ and ‘hematoma’ formation on their first attempt, respectively. Fifty-nine participants (95.2%) agreed that ability to see internal structures was useful, and 58 (93.5%, respectively) agreed that the interactivity promoted learning and that MR should be included in training. Conclusions Results of this study showed that use of a novel MR trainer for PIVC placement appears to provide an environment conducive to successful learning. Most participants were successful at PIVC placement on their first attempt and an overwhelming number found it helpful in identifying landmarks and confirming correct needle angles for insertion. Given the increasing emphasis on simulation training, highly immersive MR tools appear to offer promise to close the gap between classroom instruction and clinical experience.

No abstract

Statement: Extended reality (XR)-based simulation training offers unique features that facilitate collection of dynamic behavioral data and increased immersion/realism while providing opportunities for training health care professionals on critical events that are difficult to recreate in real life. Sequential analysis can be used to summarize learning behaviors by discovering hidden learning patterns in terms of common learning or clinical decision-making sequences. This project describes the use of sequential analysis to examine differential patterns of clinical decision-making behaviors in observed XR scenarios, allowing for new insights when using XR as a method to train for critical events and to trace clinical decision making.