ObjectiveTo introduce visual teaching into the course design of medical humanistic care based on the concept of implementation science， evaluate the teaching implementation effect and feedback， and provide references for optimizing course teaching outcomes and improving students’ humanistic care competence. MethodsA visual teaching program for medical humanistic care was designed， with key steps including clarifying teaching objectives， content， methods， and curriculum assessment. This program was implemented in the medical humanistic care course teaching involving 50 elective students. Multi-dimensional evaluation of teaching effectiveness was conducted through course grades， visual teaching evaluation， and humanistic workshop assessment， combined with inductive content analysis of students’ learning experiences in the workshops. ResultsThe 50 students achieved above-average course grades （89.60±3.41） and demonstrated high satisfaction with the overall course and visual teaching. All the 6 groups obtained relatively high scores in the medical humanistic care workshops. Four themes were extracted， namely， enhancing humanistic care competencies， deepening familial and interpersonal relationships， realizing emotional expression and self-growth， and strengthening integration of humanistic care concepts with practice. ConclusionThe teaching of medical humanistic care course has achieved favorable effects， which contributes to deepening students’ understanding of humanistic care and enhancing their humanistic care competence. Students demonstrate high levels of recognition and satisfaction with the course.

Background Construction workers represent a high risk group for occupational injuries. Currently, domestic and international studies examining the factors affecting occupational injuries among construction workers focus on demographic and behavioural characteristics. However, there is limited attention to psychosocial, use of digital technology, and health status of workers. Objective To analyze the occurrence of occupational injuries among workers in the construction industry, explore impacts of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace on occupational injuries, and provide a basis for the development of preventive measures. Methods Publicly available data from the European Union Occupational Safety and Health Administration were retrieved, comprising a sample of 2167 construction workers. The outcome indicator was the presence of occupational injuries among workers. A total of 25 variables in the dimensions of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace were extracted after Chi-square test, and then a combination of Boruta's algorithm and logistic regression was applied to identify the key factors affecting occupational injuries. Results Among the 2167 construction workers surveyed, 182 (8.6%) reported experiencing occupational injuries. The Boruta algorithm identified eight characteristics which in descending order of importance were musculoskeletal disorders, job type, depression and anxiety, level of completed education, use of electronic smart products, timely solution of safety problems, overall fatigue, and age. The logistic regression results indicated that six variables had statistically significant effects on occupational injuries: age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems (P<0.05). Conclusion Occupational injuries in construction workers are influenced by a variety of factors, including age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems. Companies and workers should take targeted measures to reduce the incidence of occupational injuries.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective To explore the effect of prominent cortical vein(PCV)sign on the clinical prognosis of patients undergoing endovascular recanalization for acute anterior circulation cerebral infarction.Methods A total of 102 patients with acute anterior circulation large-vessel occlusion in Zhongshan Municipal Hospital of Tradiontional Chinese Medicine from January 1,2020 to December 21,2022,who received endovascular recanalization,were enrolled in this study.The clinical data and radiographic materials were collected.According to whether PCV sign was present or not,the patients were divided into PCV group and non-PCV group.The clinical data were compared between the two groups.Logistic regression analysis was used to analyze the correlation between the postoperative 90-day mRS score and the occurrence of PCV.Results Compared with non-PCV group,in PCV group the incidence of atrial fibrillation was even higher,and the good rate of collateral compensation was even lower,and the postoperative(7±2)-day NIHSS score was even lower;the differences in above indexes between the two groups were statistically significant(all P＜0.05).Ninety days after treatment,the proportion of patients having modified Rankin Scale(mRS)score ≤2 points in the non-PCV group was 76.3％,which was higher than 58.6％in the PCV group,but the difference was not statistically significant(P=0.074).Logistic regression analysis indicated that a positive correlation existed between the presence of PCV sign and atrial fibrillation(OR=9.679,95％CI=1.264-74.364)and a negative correlation existed between the presence of PCV sign and good collateral compensation(OR=0.015,95％CI=0.033-0.331).No relationship existed between the postoperative 90-day good prognosis and the presence of preoperative PCV sign(OR=2.078,95％CI=0.680-6.348),but patient's age,preoperative intravenous thrombolysis were negatively correlated with the preoperative Alberta Stroke Program Early CT(ASPECT)score(P＜0.005).Conclusion In patients with anterior circulation acute ischemic stroke(AIS),the occurrence of PCV sign is associated with the impaired collateral compensation and atrial fibrillation,but its impact on the clinical outcomes of patients after receiving reperfusion therapy remains uncertain.PCV sign may be used as a potential evaluation indicator for inadequate cerebrovascular collateral compensation.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective:To describe the temporal trend of disease burden of idiopathic epilepsy in China from 1990 to 2021 and predict the incidence of idiopathic epilepsy in China from 2022 to 2035 to provide references for the formulation of relevant health policies and measures.Methods:Based on data from the Global Burden of Disease Study 2021 (GBD 2021) database regarding idiopathic epilepsy in China, changes in disease burden from 1990 to 2021 were acquired. Disease burden was quantified using age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), age-standardized disability-adjusted life years (DALYs) rate (ASDR) and their 95% uncertain interval (UI). Temporal trend analysis was performed using a linear regression model to estimate the estimated annual percent change (EAPC) and annual percentage change (APC) in incidence of idiopathic epilepsy and their 95% CI. Additionally, incidence and number of patients with idiopathic epilepsy in China from 2022 to 2035 were predicted using Bayesian age-period-cohort model. Results:The ASIR of idiopathic epilepsy increased from 22.35 per 100,000 population in 1990 (95% UI: 15.04-30.92 per 100,000 population) to 28.19 per 100,000 population in 2021 (95% UI: 19.03-37.89 per 100,000 population), with an EAPC of 0.12% (95% CI: -0.10%-0.34%); ASPR of idiopathic epilepsy increased from 189.27 per 100,000 population in 1990 (95% UI: 132.48-252.95 per 100,000 population) to 214.71 per 100,000 population in 2021 (95% UI: 150.10-278.56 per 100,000 population), with an EAPC of -0.32% (95% CI: -0.57%-0.06%); ASMR of idiopathic epilepsy decreased from 1.86 per 100,000 population in 1990 (95% UI: 1.59-2.24 per 100,000 population) to 0.80 per 100,000 population in 2021 (95% UI: 0.67-1.00 per 100,000 population), with an EAPC of -2.96% (95% CI: -3.09%-2.82%); ASDR of idiopathic epilepsy decreased from 178.60 per 100,000 population in 1990 (95% UI: 143.44-220.63 per 100,000 population) to 101.39 per 100,000 population in 2021 (95% UI: 72.51-139.40 per 100,000 population), with an EAPC of -2.38% (95% CI: -2.54%-2.22%). The prediction model showed that by 2035, the prevalence of idiopathic epilepsy in China will be 28.27 per 100,000 (95% CI: 23.19-38.66), with an estimated 394,928 incident cases (95% CI: 324,037-540,128). Conclusions:From 1990 to 2021, the ASIR and ASPR of idiopathic epilepsy in China show an upward trend, while the ASMR and ASDR hace a decline trend. Incidence of idiopathic epilepsy in China is expected to remain stable over the next decade.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.