(1) Background and Objectives: Catheter-related bladder discomfort (CRBD), a common and distressing consequence of indwelling urinary catheters, can significantly impact postoperative recovery. This study aimed to determine the effectiveness of bladder irrigation with a 0.05% lidocaine normal saline solution for the prevention of CRBD following transurethral surgery. (2) Materials and Methods: In this randomized, double-blind, placebo-controlled trial, patients were assigned to either a control group receiving normal saline or a treatment group receiving 0.05% lidocaine (2% lidocaine 25 mL in 1000 mL saline) for bladder irrigation. Both groups were administered fentanyl (1 μg/kg) for analgesia at the end of the procedure. The primary endpoint was the assessment of the incidence and severity of CRBD upon awakening within the first 6 h postoperatively, using a four-grade scale based on the patients’ reports of discomfort. (3) Results: Out of 79 patients completing the study, the incidence of moderate to severe CRBD was significantly lower in the lidocaine group (5.1%, 2/39) compared to the control group (25%, 10/40) at 10 min after waking from anesthesia (p = 0.014). Furthermore, the lidocaine group experienced significantly less CRBD at 1 and 2 h postoperative (2.6% and 0%, respectively) compared to the control group (20% and 10%, respectively) (p = 0.015, p = 0.043), with no significant differences at 6 h (p = 0.317). (4) Conclusions: The results suggest that bladder irrigation with 0.05% lidocaine reduces the occurrence of moderate to severe CRBD by nearly 80% in the initial 2 h postoperative period after transurethral surgery.

Zanthoxylum species have long been utilized in traditional medicine; among their various properties, they provide an analgesic effect. Central to this medicinal application are alkamides, a class of alkaloids characterized by their unsaturated fatty acid chains. These compounds are particularly noted for their distinctive alleviation of tingling and numbing effects, which are beneficial in dental pain management and local anesthesia. This review synthesizes the existing phytochemical research on alkamides derived from 11 Z. species, focusing on their chemical properties, pharmacodynamics and clinical implications. The analysis includes an examination of the structure–activity relationships (SARs), pharmacokinetics and mechanisms by which these compounds modulate sensations such as pungency and numbness, contributing to their analgesic and local anesthetic efficacy. This systemic review identifies significant research gaps, including the need for comprehensive evaluations of alkamide efficacy, detailed explorations of their pharmacological mechanisms and expanded clinical applications. These areas represent key opportunities for future investigations to enhance the understanding and utilization of alkamides in medical treatments.

Background and objective: Adequate postoperative pain control is an important component to enhance recovery. Multimodal analgesia with various pain control techniques has been widely used to alleviate postoperative pain. The use of either wound infiltration or a superficial cervical plexus block has been reported to be effective for pain management after thyroid surgery. The present study evaluated the effect of multimodal analgesia using lidocaine wound infiltration combined with intravenous parecoxib for patients monitored after thyroidectomy. Materials and Methods: A total of 101 patients with a multimodal analgesia protocol being monitored after thyroidectomy were enrolled. After the induction of anesthesia, multimodal analgesia was performed through wound infiltration of 1% lidocaine and epinephrine at a ratio of 1:200,000 (5 μg/mL) combined 40 mg intravenous parecoxib before skin excision. Patients were divided into two groups for this retrospective analysis based on the injection dose of lidocaine they received. Patients in Group I (the control, n = 52) received a 5 mL injection solution, while those in Group II (the study, n = 49) received a 10 mL dosage in a time-sequential manner, in accordance with a previous clinical trial. The primary outcome was measuring postoperative pain intensity at rest, as well as during motion and coughing, which was measured at the postoperative anesthetic care unit (PACU) and on the first day after the operation (POD 1) in the ward. Pain intensity was assessed using a numerical rating scale (NRS). The secondary outcomes were postoperative adverse events including anesthetic-related side effects, as well as airway and pulmonary complications. Results: Most of the patients reported no pain or mild pain during the observation period. The patients in Group II had a lower pain intensity during motion than Group I (NRS 1.47 ± 0.89 vs. 1.85 ± 0.96, p = 0.043) when measured at the postoperative anesthetic care unit. Pain intensity during coughing was also significantly lower in the study group than in the control group (NRS 1.61 ± 0.95 vs. 1.96 ± 0.79, p = 0.049) when measured at the postoperative anesthetic care unit. There were no severe adverse events in either of the groups. Only one patient (1.9%) in Group I experienced temporary vocal palsy. Conclusions: The use of lidocaine with an equal volume of intravenous parecoxib provided comparable analgesia with minimal adverse events when monitoring thyroidectomy.

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Background: Postoperative nausea and vomiting are common complications after anesthesia. However, vomiting represents a clinically distinct and objectively measurable endpoint. Objective: This study aimed to develop and internally validate predictive models for postoperative vomiting within 24 hours using structured perioperative data and unstructured clinical text, while introducing a structured framework that separates feature construction from interpretability using large language models (LLMs). Methods: We analyzed 33,460 anesthesia records from a single center (2019‐2022). Two temporally defined prediction tasks were constructed to reflect real-world clinical decision-making and prevent information leakage: a preoperative model using variables available before anesthesia induction, and a perioperative model using variables available up to the end of surgery. Structured data were modeled using machine learning algorithms (logistic regression, Extreme Gradient Boosting, Light Gradient Boosting Machine [LightGBM]). Unstructured clinical text was incorporated through a deterministic, concept-driven preprocessing pipeline, where LLMs were used solely for normalization (temperature=0) without feature generation, followed by rule-based concept mapping and feature encoding. Post hoc interpretability was further supported using an LLM-based Question Answering Chain module. Model performance was evaluated using receiver operating characteristic-area under the curve (AUC), precision-recall AUC, calibration metrics, and threshold-based operating characteristics. Classification thresholds were selected using the Youden J statistic, and all metrics were reported with 95% CIs derived from bootstrap resampling. Decision curve analysis was performed to assess clinical utility. Results: A total of 33,460 surgical procedures were included, of which 3607 (10.8%) experienced postoperative vomiting within 24 hours. In the preoperative task, LightGBM achieved an AUC of 0.729 (95% CI 0.706‐0.749), compared with 0.610 (95% CI 0.588‐0.632) for the Apfel score. In the end-of-surgery task, LightGBM achieved an AUC of 0.735 (95% CI 0.714‐0.757). At the Youden-optimal threshold, the negative predictive value exceeded 0.95 across all models. Decision curve analysis demonstrated positive net benefit across clinically relevant threshold probabilities. Incorporating text-derived features provided modest improvements, while LLM-based explanation modules generated structured, natural-language explanations intended to enhance interpretability without substantially improving predictive performance. Conclusions: Machine learning models can effectively predict postoperative vomiting within 24 hours using perioperative data. The proposed framework demonstrates that LLMs can be integrated in a controlled and reproducible manner—restricted to deterministic normalization and post hoc reasoning—to generate natural-language explanations intended to enhance the interpretability of model predictions, without introducing information leakage or altering predictive modeling. As no formal clinician-based evaluation was conducted, this interpretability benefit cannot yet be objectively confirmed, and the generated explanations should be regarded as a useful interpretability aid to be validated in future clinician-centered studies. External, multicenter validation is required before broader clinical applicability can be assumed.