Rapid onset pembrolizumab-induced inflammatory arthritis diagnosed using musculoskeletal ultrasound.

Immune checkpoint inhibitors have revolutionised the treatment of cancer. While very effective, they commonly cause a wide spectrum of immune-related adverse events. These immune-related adverse events can be fatal and often have significant effects on quality of life. They therefore require prompt recognition and management. We report the case of a woman presenting with widespread joint pain and stiffness 6 hours after her first pembrolizumab infusion. She had no joint swelling on physical examination but an ultrasound scan revealed widespread musculoskeletal inflammation, confirming the diagnosis of inflammatory arthritis. To the best of our knowledge, this is the fastest reported inflammatory arthritis onset following immune checkpoint inhibitor treatment. It highlights the importance of timely imaging in patients on immune checkpoint inhibitors who present with new non-specific musculoskeletal pain. Her symptoms improved dramatically with intramuscular triamcinolone injection.

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. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

Live streaming ward rounds using wearable technology to teach medical students: a pilot study.

Background: The COVID-19 pandemic resulted in a loss of clinical clerkship opportunities for medical students. To address this problem while maintaining patient safety, this pilot study explored the feasibility of using a wearable headset to live stream teaching ward rounds to remotely based medical students. Methods: Three live streamed teaching ward rounds were delivered to three groups of medical students (n=53) using the Microsoft HoloLens 2 device and Microsoft Teams software, and results pooled for analysis. Feedback was gathered from students and instructors using the evaluation of technology-enhanced learning materials (ETELM). Patient feedback was gathered using the Communication Assessment Tool to explore any impact on interpersonal communication. Results: The response rate for the ETELM-learner perceptions was 58% (31/53), 100% for the ETELM-instructor perceptions. Students strongly agreed that the overall quality of the teaching session and instructors was excellent. However, 32% experienced issues with audio or video quality and one remote student reported cyber sickness. The statement 'educational activities encouraged engagement with session materials/content' returned the most varied response. Instructors reported technological problems with delivery while using the HoloLens 2 device and environmental noise in the ward was a disruptive factor. Preparation and skilled facilitation were key to delivering a high-quality teaching session. Patients reacted generally favourably to the technology and no negative effects on interpersonal communication were identified. Conclusion: The experience of live streamed ward rounds was well received by patients, medical students and teaching faculty. However, there remain limitations to the routine use of HoloLens 2 technology in our setting including steep learning curves, hardware costs and environmental factors such as noise and WiFi connectivity. Live streamed ward rounds have potential postpandemic implications for the judicious use of resources, and the possibility for few educationally minded clinicians to teach at scale in a patient-friendly manner.

Aspartame in conjunction with carbohydrate reduces insulin levels during endurance exercise.

BACKGROUND: As most sport drinks contain some form of non-nutritive sweetener (e.g. aspartame), and with the variation in blood glucose regulation and insulin secretion reportedly associated with aspartame, a further understanding of the effects on insulin and blood glucose regulation during exercise is warranted. Therefore, the aim of this preliminary study was to profile the insulin and blood glucose responses in healthy individuals after aspartame and carbohydrate ingestion during rest and exercise. FINDINGS: Each participant completed four trials under the same conditions (45 min rest + 60 min self-paced intense exercise) differing only in their fluid intake: 1) carbohydrate (2% maltodextrin and 5% sucrose (C)); 2) 0.04% aspartame with 2% maltodextrin and 5% sucrose (CA)); 3) water (W); and 4) aspartame (0.04% aspartame with 2% maltodextrin (A)). Insulin levels dropped significantly for CA versus C alone (43%) between pre-exercise and 30 min, while W and A insulin levels did not differ between these time points. CONCLUSIONS: Aspartame with carbohydrate significantly lowered insulin levels during exercise versus carbohydrate alone.