Objective:To explore the application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

OBJECTIVE To summarize the current situation of pharmaceutical clinic service in our hospital over the past five years， and explore sustainable development strategies for service models of pharmaceutical clinics. METHODS A retrospective analysis was conducted on the consultation records of patients who registered and established files at the pharmaceutical clinic in our hospital from January 2019 to December 2023. Statistical analysis was performed on patients’ general information， medication- related problems， and types of pharmaceutical services provided by pharmacists. RESULTS A total of 963 consultation records were included， among which females aged 20-39 years accounted for the highest proportion （66.04%）； obstetrics and gynecology- related consultations accounted for the largest number of cases. Additionally， 80 patients attended follow-up visits at our hospital’s pharmaceutical clinic. A total of 1 029 medication-related issues were resolved， including 538 cases of drug consultations （52.28%）， 453 medication recommendations （44.02%）， 22 medication restructuring（2.14%）， and 16 medication education （1.55%）； the most common types of medication-related problems identified were adverse drug events（70.07%）. CONCLUSIONS Although the pharmaceutical clinic has achieved recognition from clinicians and patients， challenges such as low awareness among healthcare providers and the public persist. Future efforts should focus on strengthening information technology construction， enhancing pharmacist training， and establishing various forms of outpatient pharmaceutical service models.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective:To explore the application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective:To explore the genetic etiology and the value of early diagnosis of early onset epileptic encephalopathy (EOEE) with unknown etiology.Methods:A total of 60 children with EOEE of unknown etiology were prospectively enrolled in the outpatient and inpatient departments of Fujian Provincial Hospital from January 2018 to January 2021.Peripheral blood was collected prospectively for whole-exome sequencing and copy number variation (CNV) detection to analyze the clinical characteristics and genetic sequencing results of the children.Results:Twenty-four patients with EOEE-related pathogenic or suspected pathogenic mutations were detected, including infantile spasms (10 cases), Dravet syndrome (3 cases), pyridoxine-dependent epilepsy (1 case) and ohtahara syndrome (1 case), and unknown epileptic encephalopathy (9 cases). The onset age of EOEE-related patients ranged from 1 day to 11 months (median age was 4.2 months), the treatment age ranged from 2 days to 4 years (median age was 10 months), and the age of diagnosis was controlled within 1 month after treatment.There were 20 cases (33.3%) single gene variants and 4 cases (6.7%) CNV variants.A total of 13 genes were involved: KCNQ2, SCN1A, SCN8A, CACNA1E, CDKL5, PPP3CA, PCDH19, TSC1, TSC2, ZEB2, ALDH7A1, DCX and HNRNPU.The 4 CNV abnormalities were 17p13.3 deletion, 11q23.3q25 deletion, 1q36.31-p36.33 deletion, 1q43-1q44 deletion and Xp22.33 duplication, respectively.Totally, 20 mutations were new loci reported for the first time at home and abroad; 11q23.3q25 deletion that resulted in infantile spasm was first reported at home and abroad.Infantile spasm caused by ZEB2 mutation and epileptic encephalopathy caused by PPP3CA gene were both reported for the first time in China. Conclusions:Gene and CNV are important potential causes of children suffering from EOEE.When the etiology is unclear, the combination of whole-exome sequencing and CNV sequencing technology can improve the diagnosis level of genetic etiology of children with EOEE.The early genetic detection of these children can early diagnose and accurately treat epilepsy.

Objective:To investigate the demand and practical utility of simulation operations specialists (SOS) in simulation teaching modules during the standardized residency training.Methods:Based on the feedback for stimulated courses of standardized residency training, subjective evaluation of all residents, teachers and SOS who participated in simulation courses in 2017-2018 academic year were investigated and studied via the mobile phone online investigation. At the same time, the design data of teaching concept map of relevant curriculum were also included. The SPSS 13.0 was used to conduct the t test and chi-square test. Results:At present, only 26.3% of the preset functions were used in the medical simulation courses based on high-tech medical simulator. Tutors commanded less than 30% functions, while SOS participated in the whole process of the course preparation and commanded 63.6% of the course operations, which was higher than the requirement of teaching concept map (45.5%). Among them, ECG monitoring regulation, venous management and special effects makeup were in greatest needs and were items with the biggest gap between ideality and reality. Resident physicians required SOS to replace the tutors to operate teaching facilities, so as to reduce interruption (37.0%), implications (31.3%) during courses, and improvement of experience sense during the course (32.3%). Furthermore, specialists with clinical background needed more assistance from SOS than those without clinical background ( tQ3=3.204, tQ4=2.573, tQ5=2.660; P<0.05). Differences were found between the actual work content of SOS and their job requirement ( χ2=12.632, P<0.01). Conclusion:SOS plays a significant role in the simulation course of standardized residency training, especially in the course of clinical professional physicians. Auxiliary functions of simulated courses, such as teaching aids management, special effects makeup, course designing, qualified SP and others are the main necessities for SOS at present. Participation of tutors and SOS together is essential to ensure a good development and performance of medical simulation courses for standardized residency training.