Rapid cycle deliberate practice (RCDP) has emerged as an effective teaching method for enhancing skill proficiency. This study introduces the characteristics and theoretical foundations of RCDP, explains key points in instructional design and implementation strategies, and provides a comprehensive analysis of its application in medical education. Additionally, the potential value and development prospects of RCDP in simulation-based medical education in China are explored. This study aims to provide theoretical support and practical guidance for the further promotion and deeper implementation of the RCDP teaching method in China.

Objective:Based on the characteristics of grassroots disease prevention and control centers, medical institutions, and personnel involved in the prevention and control of drinking water-borne endemic fluorosis in China, the aim of this study is to develop a competency evaluation tool for grassroots personnel involved in the prevention and treatment of the disease.Methods:Through literature review and expert consultation, a competency evaluation tool was designed and developed. The tool was distributed nationwide via the Wenjuanxing platform. Valid data were collected and analyzed for item discrimination, validity, and reliability to assess its measurement effectiveness.Results:A total of 150 valid questionnaires were collected, covering 13 provinces across China (67 males, 83 females). The evaluation tool demonstrated significant differences in scores between high and low groups for all items ( P < 0.001). The correlation coefficients between each item and its corresponding factor ranged from 0.77 to 0.87, with factor loadings ranged from 0.68 to 0.97. In the validity analysis, root mean square error of approximation ( RMSEA) was 0.07, and standardized root mean square residual ( SRMR) was 0.03. The goodness-of-fit indices were as follows: goodness-of-fit index (GFI) = 0.94, normed fit index (NFI) = 0.96, incremental fit index (IFI) = 0.95, tucker-lewis index(TLI) = 0.96, and comparative fit index (CFI) = 0.96. In the reliability analysis, the overall Cronbach's α coefficient was 0.91, and the split-half coefficient was 0.88. Conclusions:The competency evaluation tool for grassroots personnel in the prevention and treatment of drinking water-borne endemic fluorosis exhibits high item discrimination, as well as good overall reliability and validity. It can be used to evaluate the competency of prevention and treatment personnel and to study influencing factors.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective:To investigate the trajectory of frailty in elderly patients on maintenance hemodialysis(MHD)following SARS-CoV-2 infection and its associated risk factors.Methods:This prospective cohort study focused on elderly patients who underwent baseline frailty assessment(T0)during hemodialysis treatment at Beijing Friendship Hospital for over 3 months between December 1st, 2022, and December 31th, 2022, and were diagnosed with SARS-CoV-2 infection.The Fried Frailty Phenotype was evaluated at 1 month(T1), 3 months(T2), and 6 months(T3)post-infection.Frailty trajectory after infection was analyzed using repeated measurement ANOVA.Patients were divided into stable/improvement or exacerbation groups based on their frailty status at T0 and T3, with logistic regression analysis employed to identify risk factors for different frailty trajectories.Results:A total of 130 elderly maintenance hemodialysis patients, with a median age of 66 years(range: 63-71 years)and 62 males(47.7%), were included in the study.Six months after the infection, a majority of surviving patients saw their frailty scores return to baseline levels.Specifically, 72 patients(55.4%)either maintained or improved to robust or pre-frail states, while 9 patients(6.9%)progressed to a pre-frail state, 18 patients(13.8%)progressed to a frail state, and 31 patients(23.8%)remained in a frail state.Results from multivariate logistic regression analysis indicated that low grip strength( OR: 6.30, 95% CI: 1.48-26.73)and all-cause hospitalization( OR: 5.01, 95% CI: 1.19-21.03)were identified as risk factors for non-frail patients transitioning to frailty( P<0.05). Conclusions:The majority of elderly maintenance hemodialysis patients who survived SARS-CoV-2 infection returned to their baseline level of frailty or showed improvement within 6 months.Non-frail patients with low grip strength or those who were hospitalized were more likely to deteriorate towards frailty.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective:To investigate the trajectory of frailty in elderly patients on maintenance hemodialysis(MHD)following SARS-CoV-2 infection and its associated risk factors.Methods:This prospective cohort study focused on elderly patients who underwent baseline frailty assessment(T0)during hemodialysis treatment at Beijing Friendship Hospital for over 3 months between December 1st, 2022, and December 31th, 2022, and were diagnosed with SARS-CoV-2 infection.The Fried Frailty Phenotype was evaluated at 1 month(T1), 3 months(T2), and 6 months(T3)post-infection.Frailty trajectory after infection was analyzed using repeated measurement ANOVA.Patients were divided into stable/improvement or exacerbation groups based on their frailty status at T0 and T3, with logistic regression analysis employed to identify risk factors for different frailty trajectories.Results:A total of 130 elderly maintenance hemodialysis patients, with a median age of 66 years(range: 63-71 years)and 62 males(47.7%), were included in the study.Six months after the infection, a majority of surviving patients saw their frailty scores return to baseline levels.Specifically, 72 patients(55.4%)either maintained or improved to robust or pre-frail states, while 9 patients(6.9%)progressed to a pre-frail state, 18 patients(13.8%)progressed to a frail state, and 31 patients(23.8%)remained in a frail state.Results from multivariate logistic regression analysis indicated that low grip strength( OR: 6.30, 95% CI: 1.48-26.73)and all-cause hospitalization( OR: 5.01, 95% CI: 1.19-21.03)were identified as risk factors for non-frail patients transitioning to frailty( P<0.05). Conclusions:The majority of elderly maintenance hemodialysis patients who survived SARS-CoV-2 infection returned to their baseline level of frailty or showed improvement within 6 months.Non-frail patients with low grip strength or those who were hospitalized were more likely to deteriorate towards frailty.

Generative Artificial Intelligence ishows a broad application prospect in the field of healthcare and has become an important technical means to promote the development of medical informatization.It addresses the multi-faceted challenges of medical record documentation,including efficiency,quality,and doctor-patient communica-tion.It analyzes the adaptability and feasibility of Generative Artificial Intelligence in different clinical scenarios of intelli-gent medical record generation.Additionally,it explores the issues present in current applications and proposes corre-sponding solutions,providing references for the effective application and continuous optimization of Generative Artifi-cial Intelligence in medical record documentation.This provides a theoretical foundation for further expanding the appli-cation scenarios of automatic medical record documentation in China's healthcare industry.

Objective To investigate the differences of brain imaging changes in patients with mesial temporal lobe epilepsy(mTLE)based on individual structural covariance networks of gray matter volume.Methods A total of 74 mTLE patients,including 39 patients in the left mTLE group and 35 patients in the right mTLE group,along with 46 healthy controls(control group),had completed 3D T1WI structural imaging scans.The network template perturbation approach was used to analyze the individualized structural covariance networks in patients with mTLE.Results Compared with the control group,the left and right mTLE groups showed decreased structural covariance connections for the ipsilateral hippocampus to the contralateral hippocampus and parahippocampal gyrus,orbitofrontal gyrus,middle frontal gyrus,superior parietal gyrus,amygdala and fusiform gyrus.In addition,increased structural covariation connections was mainly distributed in the bilateral frontal lobe,parietal lobe,temporal lobe,occipital lobe and paralimbic system in the right mTLE group,whereas increased structural covariation connections was mainly located within the left frontal lobe,parietal lobe and occipital lobe in the left mTLE group(P＜0.05).Compared with the left mTLE group,the right mTLE group showed decreased structural covariance connections with the ipsilateral hippocampus as a core node(P＜0.05).Conclusion Compared with the left mTLE group,the right mTLE group showed more pronounced decreased structural covariance connections centered around the ipsilateral hippocampus,as well as a more intricate compensatory mechanism.The pattern of the individual structural covariance networks in mTLE patients not only contribute to understanding of its pathogenesis but also served as a potential biomarker for clinical diagnosis.

Factitious Disorder is a chronic and difficult-to-diagnose artificial illness characterized by the in-dividual's recurrent simulation of symptoms,deliberate self-harm to produce signs or symptoms,and a high likeli-hood of misdiagnosis and mistreatment in clinical settings.This article introduces a case of a 10-year-old girl with factitious disorder who was misdiagnosed as Henoch-Schonlein purpura in 4 medical institutions,which was the first simulated case of Henoch-Schonlein purpura in China.Based on the literature analysis of factitious disorder,the au-thor combed and analyzed the literature in order to improve clinicians'awareness of the disease and reduce misdiag-nosis and mistreatment.

Objective The mechanism of Huazhuo xingxue decoction(HZXXD)in the treatment of ischemic stroke was explored through network pharmacology,molecular docking and cell validation.Methods TCMSP,TCMID,BATMAN-TCM database and literature search were used to get the chemical components and related target proteins of Huazhuo Xingxue Decoction,and the targets of dementia,stroke and amnesia were obtained from Genecards database and OMIM database.The traditional Chinese medicine-active components-target-network and protein interaction map were constructed by using Cytoscape,and the target was enriched by KEGG pathway by David database.Western blot was used to investigate the effect of HZXXD on inflammation-related core targets expression using oxygen and glucose deprivation/reoxygenation cell model.Finally,Autodock was used for molecular docking of key active ingredients and important targets to evaluate their binding activity.Results 76 active molecules and 33 common targets of herb-disease were screened out.KEGG bioaccumulation results involve multiple inflammatory signal pathways such as TNF,chemical carcinogenesis-reactive oxygen species and HIF-1.TNF-α was found to be the core target of HZXXD by oxygen glucose deprivation/reoxygenation cell experiments.Five compounds with the strongest binding ability to TNF-α,kaempferol,apigenin,aloe-emodin,baicalein and stigasterol,were screened by traditional Chinese medicine-active ingredient-target network map and molecular docking.Conclusion Huazhuo Xingxue Decoction may down regulate the expression of core target TNF-α,kaempferol,apigenin,aloe emodin,baicalein and stigasterol may be the main active substances for TNF-α binding.