Objective:To analyze the current policy design of outpatient benefit policies for patients with hypertension and diabetes(H&D)in urban and rural residents in China,and to provide references for optimizing the outpatient benefit mechanism for patients with H&D.Methods:A questionnaire survey was conducted to collect data on the basic features of General Outpatient Benefit(GOB),Outpatient Medication Guarantee Mechanisms for Hypertension and Diabetes(OMGM-H&D)and Outpatient Benefit for Patient with Chronic and Special Diseases(OB-C&S),focusing on deductibles,policy reimbursement ratios,and maximum payment limits.Descriptive statistical analysis was performed on the data.Results:A total of 334 regions were surveyed,of which 253 regions(75.34％)had implemented all three policies.Regional analysis revealed significant differences(P<0.001)in reimbursement ratios for GOB and the OMGM-H&D between the eastern,central,and western regions,with the western region having notably higher ratios than the eastern and central regions.However,no significant difference (P>0.05) was observed in the reimbursement ratios for OB-C&S across regions. Regarding deductibles,no significant regional difference (P>0.05) was found for GOB,but significant differences (P<0.05) existed for the OMGM-H&D and OB-C&S. Additionally,the annual maximum payment limits for all three policies showed significant regional variations (P<0.001). Conclusions:The outpatient benefits policy for patients with H&D have achieved full coverage nationwide among urban and rural residents in China,but regional equity in benefit levels requires improvement. It is recommended to strengthen data feedback mechanisms and promote provincial-level pooling of medical insurance.

Objective To retrospectively analyze the association between metabolic factors and high-risk colorectal adenoma(CRA).Methods The medical records of patients aged 18-75 years who underwent their initial colonoscopy at Karamay Central Hospital of Xinjiang Uygur Autonomous Region from Jul 2000 to Mar 2017 were collected.The comparison between normal colonoscopy(NC)and high-risk CRA patients was conducted using an unpaired t-test,while chi-square test was used for categorical variables.Least absolute shrinkage and selection operator(LASSO)regression and Logistic regression were utilized to analyze the association between metabolic factors and high-risk CRA.Results A total of 1 798 patients meeting the inclusion and exclusion criteria were enrolled and divided into normal colonoscopy(NC)findings group(n=972)and high-risk CRA group(n=826).The high-risk CRA group exhibited significantly lower levels of high-density lipoprotein cholesterol(HDL-C)in comparison to the NC group,while uric acid and fibrosis 4(FIB-4)index levels were significantly higher than those observed in the NC group(all P<0.05).Based on LASSO regression analysis,we identified 12 variables that potentially influence the occurrence of high-risk CRA,including age,gender,smoking history,alcohol consumption history,non-alcoholic fatty liver disease(NAFLD),hypertension,coronary artery disease,hyperglycemia,hypercholesterolemia,low levels of HDL-C,elevated alanine aminotransferase,and elevated gamma-glutamyl transferase.Multivariate analysis revealed that individuals aged over 50 years,male gender,cigarette and alcohol consumption,low HDL-C levels,history of NAFLD and hypertension were identified as independent risk factors associated with high-risk CRA(P<0.05).In addition,without or with adjusting for age,sex,smoking,and drinking history,patients with a high TG/HDL-C ratio(the ratio≥2.68)had a significantly higher risk of high-risk CRA than those with a low TG/HDL-C ratio(the ratio<2.68)[odds ratios(ORs)were1.430 and 1.235 respectively,all P<0.05)].Without or with adjusting variables,the ORs for NAFLD patients with FIB-4 index>2.67 were 1.849(P=0.466)and 1.435(P=0.707),respectively.Conclusion A significant association exists between metabolic factors and high-risk CRA.Independent risk factors for high-risk CRA include older age(≥50 years),male,smoking history,alcohol consumption history,low levels of HDL-C,and a history of NAFLD and hypertension.Individuals exhibiting a TG/HDL-C ratio exceeding 2.68 manifest a significantly heightened susceptibility to the development of high-risk CRA.Therefore,elderly males with one or more aforementioned metabolic abnormalities should be considered a priority population for colorectal screening.

Objective To explore the effects of behavior change intervention based on the multi-ple-theory model on patients with dyslipidemic ischemic stroke.Methods A total of 93 patients with dyslipidemic ischemic stroke who were hospitalized in the vascular neurology ward of Beijing Tiantan Hospital,Capital Medical University from January to August 2024 were selected as the study subjects using the convenience sampling method.They were randomly divided into control group(n=49)and intervention group(n=44)using the envelope-drawing method.Patients in the control group re-ceived routine stroke health education,while those in the intervention group underwent a 3-month be-havior change program guided by the multiple-theory model.The levels of healthy behaviors,body mass index(BMI),total cholesterol(TC),triglycerides(TG),high-density lipoprotein(HDL-C),and low-density lipoprotein(LDL-C)were compared between the two groups.Results There were no statistically significant differences in general information and disease-related data between the two groups(P＞0.05).At 1-,3-,and 6-month after the intervention,the level of healthy behaviors in the intervention group was higher than that in the control group,with statistically significant differ-ences(P＜0.05).There was a statistically significant difference in BMI between the two groups at 6 months after the intervention(P＜0.05).The TC levels in the intervention group at 3 and 6 months after the intervention were lower than those in the control group,with statistically significant differences(P＜0.05).The HDL-C level in the intervention group at 6 months after the intervention was high-er than that in the control group,with a statistically significant difference(P＜0.05).The LDL-C levels in the intervention group at 1-,3-,and 6-month after the intervention were lower than those in the control group,with statistically significant differences(P＜0.05).There were no statistically significant differences in TG levels between the intervention group and the control group at different time points after the intervention(P＞0.05).Conclusion The behavior change intervention pro-gram based on the multiple-theory model can effectively improve and maintain healthy behaviors and improve blood lipid levels in patients with dyslipidemic ischemic stroke.

Objective To explore the effectiveness of an integrated laparoscopic simulation training course (best essential surgical technique training, BEST) in enhancing laparoscopic peritoneal suturing techniques in surgical residents.Methods As an integrated two-stage program, the BEST course applied basic laparoscopic training system with simple molds in phase Ⅰ training, and then adopted advanced laparoscopic training system, 3D Laparoscope and ex-vivo animal models in phase Ⅱ training. The laparoscopic suturing techniques were practiced in phase Ⅱ training. From August 2021 to July 2024, surgical residents in the second year of the national standardized training program were divided into pilot and control groups based on whether they had undergone the BEST course. Two cases of laparoscopic peritoneal suture were performed by the surgical residents under supervision in the department of gastrointestinal surgery. The operative time, quality of suture, and independent completion rate were compared between the two groups.Results A total of 33 surgical residents (19 in pilot group and 14 in control group) were included in this study, and a total of 66 cases of laparoscopic peritoneal suture were performed (38 in pilot group and 28 in control group). The operative time was significantly shorter in pilot group than that in control group (15.7 min vs. 17.5 min, P=0.025). The quality of suture was significantly better in pilot group compared to control group (P=0.023). In pilot group, all peritoneal sutures were performed by residents independently, whereas in control group, 3 cases (10.7%) were assisted by the supervisor, and the independent completion rate was different significantly (P=0.039).Conclusions The BEST course can help improve surgical residents′ laparoscopic peritoneal suturing techniques and could be promoted in the national standardized training program for surgical residents.

Objective:This study aims to evaluate the effectiveness of the 4C continuous care model in home enteral nutrition management for patients after gastric cancer surgery.Methods:A control group consisting of 40 gastric cancer patients who were hospitalized in the Department of Gastrointestinal Surgery of Suzhou Ninth People's Hospital from January 2022 to June 2022 was chosen.This group received traditional nutritional management guidance,which included nutritional screening and assessment,nutritional monitoring,providing personalized nutritional guidance for each patient,and follow-ups via telephone and outpatient visits.An observation group consisting of 40 gastric cancer patients admitted between July 2022 and January 2023 received the same basic care as the control group,with the addition of the 4C continuous care plan based on an internet platform to improve their enteral nutrition status.The nutritional indicators of both groups were compared at 1 month,3 months,and 6 months post-intervention.Additionally,the results of the Nutritional Risk Screening(NRS 2002)and Patient-Generated Subjective Global Assessment(PG-SGA)at discharge and 6 months post-discharge were compared between the two groups.Results:At 1 month,3 months,and 6 months post-intervention,the observation group showed an increase in indicators such as albumin,prealbumin,hemoglobin,upper limb grip strength,and triceps skinfold thickness compared to the control group(P<0.05).At 6 months post-intervention,the PG-SGA and NRS 2002 scores in the observation group were improved compared to the control group(P<0.05).Conclusion:The implementation of the 4C continuous care model on an internet platform significantly improves home enteral nutrition status for patients after gastric cancer surgery.

Objective:To standardize the management of Investigator-Initiated Trials(IITs) and improve the quality of research projects, this study takes a tertiary hospital in Beijing as an example to analyze the key risk points in the implementation process of IITs and proposes countermeasures based on the issues identified during the management process.Methods:The study analyzed the IITs conducted at in the hospital from 2022 to 2023, focusing on 4 aspects: project classification evaluation and management (risk management), project implementation quality (process management), collaboration and support conditions(contract review and execution), and participant protection (medical ethics). It examined the key points and difficulties in project process management to standardize the quality management of IITs.Results:The implementation process management of IITs in medical institutions was an essential component for standardized clinical research management and an effective means to ensure the scientific nature of clinical research and the quality of data.Conclusions:Medical institutions should establish an effective and feasible IIT quality management system to comprehensively enhance the quality of IIT project, aiming to produce high-quality clinical research outcomes.

Objective:To investigate the longitudinal association between the Chinese Visceral Adiposity Index(CVAI) and cardiovascular disease(CVD) risk in middle-aged and elderly individuals with abnormal glucose metabolism.Methods:A total of 3 662 individuals with abnormal glucose metabolism from the China Health and Retirement Longitudinal Study(CHARLS) database were included. The cumulative incidence of CVD events was estimated using Kaplan-Meier curves. Cox proportional hazards models were used to assess the association between CVAI and CVD events, and a multivariate-adjusted restricted cubic spline(RCS) curve was fitted to test the dose-response relationship between CVAI and incident CVD events. Subgroup analyses were also conducted to explore potential effect modifiers. Results:During the follow-up, 844 participants experienced CVD events. In the fully adjusted model, every interquartile range( IQR) increment in CVAI was significantly associated with an increased risk of CVD events, with a hazard ratio( HR)[95% confidence interval( CI)] of 1.22(1.11-1.35). Compared to the first quartile( Q1) of CVAI, the HRs(95% CI) for CVD event incidence in Q2- Q4 were 1.22(0.98-1.52), 1.29(1.05-1.61), and 1.51(1.22-1.88), respectively. Subgroup analysis indicated a significant interaction effect between CVAI and age on the risk of CVD events in individuals with abnormal glucose metabolism( P for interaction <0.05). No significant interactions were found for gender, body mass index(BMI), waist circumference(WC), smoking, alcohol consumption, and hypertension(all P for interaction >0.05). Furthermore, the multivariate-adjusted RCS regression revealed a significant dose-response relationship between CVAI and CVD events( P<0.001, P for nonlinearity=0.568). Conclusions:In middle-aged and elderly individuals with abnormal glucose metabolism, CVAI is independently associated with an increased risk of incident CVD events and may serve as a reliable surrogate marker for assessing visceral adiposity in this population. Regular monitoring of CVAI should be considered for integration into routine clinical practice to improve CVD prevention in individuals with abnormal glucose metabolism.

Objective:To develop an automatic diagnostic model for temporomandibular joint osteoarthritis(TMJOA)imaging based on deep learning technology,and to assist clinical diagnosis and improve the efficiency and accuracy of TMJOA diagnosis.Methods:CBCT data of 220 patients were collected,and 2 052 sagittal images were exported.Regions of interest were delineated according to the imaging analysis criteria for temporomandibular joint disorders,and the images were classified into TMJOA-free,TMJOA-uncer-tain and TMJOA.The data were randomly divided into a training set and a validation set according to 8∶2 ratio,and the training set data were used to train a TMJOA detection model based on three lightweight YOLOV5 deep learning frameworks,and the models' performance was evaluated on the validation set.Results:The Yolov5N model demonstrated the best performance,achieving a de-tection accuracy,recall,and precision of 92.5％,90.1％and 85.7％on the validation set,respectively.Conclusion:The auto-matic detection model for TMJOA imaging developed in this study can effectively identify arthritic lesions.Artificial intelligence tools are expected to become a powerful auxiliary tool for the clinical diagnosis of TMJOA.

Objective:To construct the Primary Health Development Index(PHDI)and measure the performance of primary health care development in China.Methods:The PHDI was established through expert consultation.The indicator weights were determined using a comprehensive weighting method.Spatial autocorrelation and regional disparities in PHDI were analyzed using Moran's I and Theil indices.Results:(1)The PHDI framework comprises three dimensions—public accountability,health resources,and integrated services—covering 14 indicators.(2)The PHDI exhibited sustained growth,increasing from 70.46 in 2018 to 83.02 in 2022,with an average annual growth rate of 4.19%.(3)Spatial clustering of PHDI was observed,where provinces with high(low)scores neighbored provinces with similarly high(low)scores,though this positive spatial correlation gradually weakened.(4)Regional disparities in primary health care development showed continuous narrowing,with intra-regional differences dominating overall disparities.Intra-regional variations exhibited as"Eastern＞Western＞Central".Conclusions and suggestions:China's primary health care system has made rapid progress,with a growing trend toward more equitable access.However,disparities within regions persist.It is recommended to routinize and institutionalize the monitoring and evaluation of primary health care development indicators,enhance evidence-based policy implementation,strengthen inter-provincial collaboration within regions,and promote coordinated resource allocation to support balanced development.

Objective:To develop an automatic diagnostic model for temporomandibular joint osteoarthritis(TMJOA)imaging based on deep learning technology,and to assist clinical diagnosis and improve the efficiency and accuracy of TMJOA diagnosis.Methods:CBCT data of 220 patients were collected,and 2 052 sagittal images were exported.Regions of interest were delineated according to the imaging analysis criteria for temporomandibular joint disorders,and the images were classified into TMJOA-free,TMJOA-uncer-tain and TMJOA.The data were randomly divided into a training set and a validation set according to 8∶2 ratio,and the training set data were used to train a TMJOA detection model based on three lightweight YOLOV5 deep learning frameworks,and the models' performance was evaluated on the validation set.Results:The Yolov5N model demonstrated the best performance,achieving a de-tection accuracy,recall,and precision of 92.5％,90.1％and 85.7％on the validation set,respectively.Conclusion:The auto-matic detection model for TMJOA imaging developed in this study can effectively identify arthritic lesions.Artificial intelligence tools are expected to become a powerful auxiliary tool for the clinical diagnosis of TMJOA.