Efficacy of Anxiolytic and Preoperative Counseling on Preoperative Anxiety Reduction: A Randomized Comparison Study

Background: The incidence of preoperative anxiety is high. Anxiolytic agents have been used to reduce preoperative anxiety for many years. Recently the role of non-pharmacological interventions like preoperative information through interviews, counseling, and verbal reassurance for the reduction in preoperative anxiety has been established. But, the efficacy of these non-pharmacological interventions and the anxiolytic agent has not been compared previously. In this study, we compared the effectiveness of oral alprazolam and preoperative counseling by an anesthesiologist for the reduction of preoperative anxiety. Materials and Methods: A total of 110 patients were randomly divided into two groups. Group A received 0.5 mg oral Alprazolam tablets, the night before surgery. Group C received counseling from an anesthesiologist with a fixed protocol the night before surgery. Anxiety was assessed with a state-trait anxiety inventory (STAI) scale, and scores were assessed in the pre-anesthetic assessment room, at night before surgery before giving study interventions, and on the day of surgery before shifting to the operation room. Hemodynamic parameters and respiratory rate were monitored along with anxiety scores. Data were analyzed using an independent t-test, Chi-square test, and repeated variance analysis measures (ANOVA). Results: Anxiety scores and hemodynamic parameters were comparable between the groups at all times of measurement. Anxiety scores in the alprazolam group were less than counseling group on the day of surgery, but this difference was not statistically significant. Conclusion: Although treatment with Alprazolam resulted in lesser anxiety scores, overall, both the methods were ineffective in reducing preoperative anxiety

Trust, But Verify: A Survey of Randomized Smoothing Techniques

Machine learning models have demonstrated remarkable success across diverse domains but remain vulnerable to adversarial attacks. Empirical defence mechanisms often fall short, as new attacks constantly emerge, rendering existing defences obsolete. A paradigm shift from empirical defences to certification-based defences has been observed in response. Randomized smoothing has emerged as a promising technique among notable advancements. This study reviews the theoretical foundations, empirical effectiveness, and applications of randomized smoothing in verifying machine learning classifiers. We provide an in-depth exploration of the fundamental concepts underlying randomized smoothing, highlighting its theoretical guarantees in certifying robustness against adversarial perturbations. Additionally, we discuss the challenges of existing methodologies and offer insightful perspectives on potential solutions. This paper is novel in its attempt to systemise the existing knowledge in the context of randomized smoothing