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This review describes an in-depth analysis of the neurotoxicity associated with the anesthetic agents used during fetal surgery, intending to highlight the importance of understanding the effects of general anesthetics on the developing brain, particularly in the context of open fetal surgery, where high doses are applied to facilitate surgical access and augment uterine relaxation. We examined evidence from preclinical studies in rodents and primates, along with studies in human subjects, with the results collectively suggesting that general anesthetics can disrupt brain development and lead to long-lasting neurological deficits. Our review underscores the clinical implications of these findings, indicating an association between extensive anesthetic exposure in early life and subsequent cognitive deficits. The current standard of anesthetic care for fetal surgical procedures was scrutinized, and recommendations have been proposed to mitigate the risk of anesthetic neurotoxicity. These recommendations emphasize the need for careful selection of anesthetic techniques to minimize fetal exposure to potentially harmful agents. In conclusion, while the benefits of fetal surgery in addressing immediate risks often outweigh the potential neurotoxic effects of anesthesia, the long-term developmental impacts nevertheless warrant consideration. Our analysis suggests that the use of general anesthetics in fetal surgery, especially at high doses, poses a significant risk of developmental neurotoxicity. As such, it is imperative to explore safer alternatives, such as employing different methods of uterine relaxation and minimizing the use of general anesthetics, to achieve the necessary surgical conditions. Further research, particularly in clinical settings, is essential to fully understand the risks and benefits of anesthetic techniques in fetal surgery.

Background: Interventional pain treatment centers represent an integral part of interdisciplinary care. Barriers to effective treatment include access to care and financial issues related to pain clinic operations. To address these challenges, specialty clinics have taken steps to identify and remedy missed clinic appointments. However, no prospective study has sought to identify factors associated with pain clinic "no-shows.", Methods: We performed a prospective, longitudinal year-long study in an inner-city, academic pain clinic in which patients scheduled for office visits and procedures were categorized as to whether they showed up or did not show up for their scheduled appointment without cancelling the day before. Twenty demographic (age, employment status), clinical (eg, diagnosis, duration of pain), and environmental (season, time and day of appointment) variables were assessed for their association with missing an appointment. The logistic regression model predicting no-shows was internally validated with crossvalidation and bootstrapping methods. A predictive nomogram was developed to display effect size of predictors for no-shows., Results: No-show data were collected on 5134 patients out of 5209 total appointments for a capture rate of 98.6%. The overall no-show rate was 24.6% and was higher in individuals who were young (<65 years), single, of ethnic minority background, received Medicare/Medicaid, had a primary diagnosis of low back pain or headaches, were seen on a day with rain or snow or for an initial consult, and had at least 1 previous pain provider. Model discrimination (area under curve) was 0.738 (99% confidence interval, 0.70-0.85). A minimum threshold of 350 points on the nomogram predicted greater than 55% risk of no-shows., Conclusions: We found a high no-show rate, which was associated with predictable and unpredictable (eg, snow) factors. Steps to reduce the no-show rate are discussed. To maximize access to care, operation managers should consider a regression model that accounts for patient-level risk of predictable no-shows. Knowing the patient level, no-show rate can potentially help to optimize the schedule programming by staggering low- versus high-probability no-shows.