International journal of obstetric anesthesia
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Int J Obstet Anesth · Dec 2024
Case ReportsSpinal anesthesia for cesarean delivery in a laboring patient with known cranial arachnoid cyst: a case report.
Arachnoid cysts are fluid-filled cavities that are usually asymptomatic and do not require surgical intervention. However, there are concerns and limited literature on the safety of neuraxial procedure in obstetric patients with cranial arachnoid cysts. ⋯ After a multidisciplinary discussion, it was concluded that neuraxial labor anesthesia and labor would be appropriate. Ultimately, the patient received spinal anesthesia for cesarean delivery for fetal intolerance to labor.
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Int J Obstet Anesth · Dec 2024
Novel bimanual haptic simulator for epidural loss-of-resistance detection: a pilot study assessing movement strategies and performance across anesthesiologist experience levels.
Correct identification of the epidural space requires extensive training for technical proficiency. This study explores a novel bimanual haptic simulator designed for the precise insertion of an epidural needle based on loss-of-resistance (LOR) detection, providing realistic dual-hand force feedback. ⋯ The innovative bimanual haptic simulator shows significant potential as a tool for assessing epidural skills and differentiating expertise levels. Its ability to provide realistic, concurrent feedback for both hands, adapt to patient anatomical variations, and generate precise metrics for performance evaluation distinguishes it from existing simulators. However, further research is necessary to establish its value as a training tool. Planned studies will focus on developing an effective training protocol and evaluating the long-term educational impact of the simulator, determining whether its integration into residency programs can improve patient outcomes.
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Int J Obstet Anesth · Dec 2024
Readability, quality and accuracy of generative artificial intelligence chatbots for commonly asked questions about labor epidurals: a comparison of ChatGPT and Bard.
Over 90% of pregnant women and 76% expectant fathers search for pregnancy health information. We examined readability, accuracy and quality of answers to common obstetric anesthesia questions from the popular generative artificial intelligence (AI) chatbots ChatGPT and Bard. ⋯ Bard readability scores were high school level, significantly easier than ChatGPT's college level by all scoring metrics (P <0.001). Bard had significantly longer answers (P <0.001), yet with similar accuracy of Bard (85 % ± 10) and ChatGPT (87 % ± 14) (P = 0.5). PEMAT understandability scores were no statistically significantly different (P = 0.06). Actionability by PEMAT scores for Bard was significantly higher (22% vs. 9%) than ChatGPT (P = 0.007) CONCLUSION: Answers to questions about "labor epidurals" should be accurate, high quality, and easy to read. Bard at high school reading level, was well above the goal 4th to 6th grade level suggested for patient materials. Consumers, health care providers, hospitals and governmental agencies should be aware of the quality of information generated by chatbots. Chatbots should meet the standards for readability and understandability of health-related questions, to aid public understanding and enhance shared decision-making.