Background: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthesia management in obstetric surgery. Material and methods: This prospective study included 763 obstetric patients who underwent elective cesarean delivery between December 2023 and May 2024. A machine-learning model for postoperative analgesia and adverse reactions (MLPIAAR) was constructed using 42 variables categorized into preoperative (18), intraoperative (12), and postoperative (12) factors. Ten algorithms were applied for model development. Model performance was optimized through 10-fold cross-validation and GridSearchCV. Evaluation metrics included receiver operating characteristic, area under the curve, accuracy, and precision. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Regression analysis explored PCA compression frequency, consumption, and satisfaction of PCA factors. Validation was performed using predicted values and scatter plots. Results: Our study found 24.25% of patients experienced inadequate postoperative analgesia within 24 h. Postoperative nausea also occurred in 23.20% of patients. Simultaneously using dexamethasone and flurbiprofen axetil reduced both risks. However, pregnancy-induced hypertension and intraoperative shivering increased nausea risk. Postoperative vomiting occurred in 11.80% of patients, primarily associated with intravenous PCA type. Hydromorphone PCA showed a higher vomiting incidence than nalbuphine PCA. Intraoperative nausea and vomiting increased postoperative vomiting risk. Patients receiving diclofenac sodium and those with longer surgeries exhibited higher PCA compression frequencies and consumption. Higher preoperative Edinburgh Postnatal Depression Scale scores correlated with increased PCA consumption. Conclusions: The MLPIAAR model predicted high-risk factors for inadequate analgesia, nausea, and vomiting in obstetric patients. Dexamethasone and flurbiprofen axetil reduced these risks, while hydromorphone PCA increased vomiting risk compared to nalbuphine PCA. Machine learning and SHAP are valuable for optimizing anesthesia and analgesia management in obstetrics.

Background: Simulation-based training is used to improve fiberoptic bronchoscopic skills for novices. We developed a nonanatomical task trainer (named 12-hole clock model) that focused on training manipulation of bronchoscopes. The aim of this study was to evaluate the training effect of this model on bronchoscopic skills and learning interests in simulated normal and difficult airways among anesthesia residents. Methods: Forty-three anesthesia residents without experience in bronchoscopic intubation were randomly divided into control (n = 22) and intervention groups (n = 21). All participants received standard multimedia learning and a baseline test using a normal airway manikin. Then, the control and intervention groups engaged in 60 minutes of training via a traditional airway manikin or the clock model, respectively. After training, the participants completed bronchoscopic performance assessments in simulated normal and difficult airways, as well as an electronic questionnaire related to the course. Results: During training, the total hands-on time of bronchoscopic practice recorded by trainees’ themselves was longer in the intervention group than in the control group (1568 ± 478 seconds vs 497 ± 172 s, P < .0001). Posttraining, the time required to visualize the carina in simulated normal airways was longer in the intervention group than in the control group (22.0 [18.0, 29.0] vs 14.0 [10.8, 18.3], P < .0001), while it was shorter for simulated difficult airways (24.0 [16.0, 32.0] s vs 27.0 [21.0, 35.5] s, P = .0425). The survey results indicated that confidence in bronchoscopic intubation increased in both groups, without significant differences in satisfaction, acceptance, or perceived difficulty between the groups. However, the interest ratings were higher in the intervention group than in the control group. Conclusions: The 12-hole clock model is a simple and feasible method for improving bronchoscopic skills and promoting interest among trainees. Trial registration: NCT05327842 at Clinicaltrials.gov.

Background: Chronic postsurgical pain (CPSP) poses a major clinical challenge due to unresolved links between neurotrophic pathways and endoplasmic reticulum (ER) stress. While Neurotrophic Tyrosine Kinase Receptor Type 1 (NTRK1) modulates ER stress in neuropathic pain, its interaction with Insulin-Like Growth Factor II (IGF2) in CPSP remains uncharacterized, impeding targeted therapy. This study defined the spinal NTRK1-IGF2-ER stress axis in CPSP. Methods: Using a skin/muscle incision–retraction (SMIR) rat model, we integrated molecular analyses and intrathecal targeting of NTRK1 (GW441756) or IGF2 (siRNA). Results: SMIR surgery upregulated spinal NTRK1, IGF2, and ER stress mediators. NTRK1 inhibition reduced both NTRK1/IGF2 expression and ER stress, reversing mechanical allodynia. IGF2 silencing attenuated ER stress and pain but did not affect NTRK1, revealing a unidirectional signaling cascade where NTRK1 drives IGF2-dependent ER stress amplification. These findings expand understanding of stress-response networks in chronic pain. Conclusions: We show that spinal NTRK1 drives IGF2-mediated ER stress to sustain CPSP. The NTRK1-IGF2-ER stress axis represents a novel therapeutic target; NTRK1 inhibitors and IGF2 biologics offer non-opioid strategies for precision analgesia. This work advances CPSP management and demonstrates how decoding unidirectional signaling hierarchies can transform neurological disorder interventions.