1.Clinical application of case teaching method combined with clinical pathway in pediatrics teaching
Xiongfei ZHOU ; Xuewen CHEN ; Juan HUANG ; Kaixin MAO
Chinese Journal of Medical Education Research 2018;17(7):742-747
Objective To explore the clinical application of case teaching method combined with clinical pathway in pediatrics teaching.Methods 103 students in pediatric practice from September 2016 to September 2017 were enrolled as the study object,who were divided randomly into observation group (52 cases) and control group (51 cases).Routine teaching method was adopted in the control group,while case teaching method combined with clinical pathway was adopted in the observation group.In addition,100 cases of children in need of rescue were selected for each group and the students were divided into two or three in a group to participate in the rescue.If the student could complete the rescue with the assistance of supervisor,he will be considered as playing a leading role in rescue process.Otherwise,another supervisor would join to lead the rescue,and the student would assist by the side,who will be considered as playing an auxiliary role in rescue process.The results of examination,classroom participation,homework,the rescue performance and the satisfaction of teaching were compared between two groups.Results The scores of theoretical course,case analysis and skill operation in the observation group was significantly higher than that of the control group (P<0.05).The scores of classroom attendance,classroom activity and homework quality in the observation group was significantly higher than that of the control group (P<0.05).Success rate of rescue conducted by students in the observation group was significantly higher than that in the control group(P<0.05).The teaching satisfaction of the observation group was also significantly higher than that of the control group (P<0.05).Conclusion The application of case teaching method combined with clinical pathway teaching model in pediatrics teaching is effective and worthy of popularizing,which can obviously improve teaching quality,arouse students' interest in teaching process,and improve students' professional quality.
2.Value of CT radiomics features for predicting radiation pneumonitis in esophageal cancer patients treated with intensity-modulated radiotherapy
Kaixin LI ; Runzhi MAO ; Bingzong GAO ; Yayun CHEN ; Wenjie CAI
Chinese Journal of Radiation Oncology 2023;32(11):978-983
Objective:To construct a predictive nomogram incorporating pretreatment CT-based radiomics for radiation pneumonitis (RP) in esophageal cancer (EC) patients treated with intensity-modulated radiotherapy (IMRT), and to evaluate the value of CT radiomics in predicting RP.Methods:Clinical data of 267 EC patients sequentially treated with IMRT in Quanzhou First Hospital affiliated to Fujian Medical University from January 2019 to December 2021 were prospectively analyzed. Among them, the first 206 patients were assigned into the training cohort and the last 61 patients were enrolled in the validation cohort. Radiomics features of bilateral lungs were extracted by radiotherapy CT simulation. Univariate analysis was performed to screen the potential predictive variables for symptomatic RP. Machine learning algorithms, such as least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGboost), and support vector machine (SVM), were performed for radiomic features selection, respectively. The best classifier was chosen to construct a radiomic signature (RS). Clinical, radiomics and combined nomogram predictive model were developed, respectively. The predictive efficiency and clinical benefits of three models were compared by calculating the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and decision curve analysis (DCA), and then validated in the validation cohort. Multivariate logistic regression analysis was conducted. Different ROC curves were compared by Delong test.Results:Cardiovascular disease, minimum internal diameter of esophagus and adjuvant chemotherapy and RS were the independent related factors of RP. The AUC of clinical, radiomics and combined models were 0.772, 0.745, 0.842 in the training cohort, and 0.851, 0.811, 0.901 in the validation cohort, respectively. DCA showed that combined radiomic model yielded better clinical benefits compared with clinical model.Conclusion:Radiomics features from pretreatment CT have the potential of improving the efficiency of RP prediction models for EC patients treated with IMRT.