Objective:To explore the feasibility of selecting appropriate respiratory motion management strategies for patients undergoing stereotactic radiotherapy for liver cancer using chi-square feature testing and machine learning techniques.Methods:A retrospective analysis was conducted on 95 liver cancer patients who underwent respiratory motion evaluation at Xiangya Hospital of Central South University between March 2022 and August 2024. Chi-square testing was used to screen features related to respiratory motion evaluation in liver cancer patients. Based on these features, predictive models were constructed using 4 machine learning classification algorithms: support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost). The predictive performance of these models was evaluated using accuracy, sensitivity, specificity, F1 value, and area under the curve (AUC).Results:The accuracy values for the SVM, RF, GBDT and AdaBoost models were 0.75, 0.75, 0.70, and 0.82, respectively. The sensitivity values were 0.82, 0.82, 0.64, and 0.82, respectively. The specificity values were 0.63, 0.63, 0.63, and 0.75, respectively. The F1 scores were 0.78, 0.78, 0.67, and 0.82, respectively. The AUC values were 0.85, 0.80, 0.76, and 0.85, respectively.Conclusions:The predictive models constructed by combining chi-square feature testing and machine learning methods can effectively predict the selection of respiratory motion management strategies. Among the models, the AdaBoost model demonstrated the best predictive performance for selecting respiratory motion management strategies.

Objective:To explore the feasibility of selecting appropriate respiratory motion management strategies for patients undergoing stereotactic radiotherapy for liver cancer using chi-square feature testing and machine learning techniques.Methods:A retrospective analysis was conducted on 95 liver cancer patients who underwent respiratory motion evaluation at Xiangya Hospital of Central South University between March 2022 and August 2024. Chi-square testing was used to screen features related to respiratory motion evaluation in liver cancer patients. Based on these features, predictive models were constructed using 4 machine learning classification algorithms: support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost). The predictive performance of these models was evaluated using accuracy, sensitivity, specificity, F1 value, and area under the curve (AUC).Results:The accuracy values for the SVM, RF, GBDT and AdaBoost models were 0.75, 0.75, 0.70, and 0.82, respectively. The sensitivity values were 0.82, 0.82, 0.64, and 0.82, respectively. The specificity values were 0.63, 0.63, 0.63, and 0.75, respectively. The F1 scores were 0.78, 0.78, 0.67, and 0.82, respectively. The AUC values were 0.85, 0.80, 0.76, and 0.85, respectively.Conclusions:The predictive models constructed by combining chi-square feature testing and machine learning methods can effectively predict the selection of respiratory motion management strategies. Among the models, the AdaBoost model demonstrated the best predictive performance for selecting respiratory motion management strategies.

Fetal monitoring is an essential component of the prenatal examination. With electronic fetal heart monitoring, clinicians can effectively monitor the intrauterine situation of the fetus, promptly detect fetal distress, and intervene early to reduce the occurrence of adverse outcomes in newborns. In recent years, the leaps in internet technology have enabled the widespread utilization of remote electronic fetal heart monitoring based on ultrasound technology. This paper reviews the application, effectiveness, and safety of remote fetal heart monitoring, and the satisfaction level of healthcare professionals with this technology in recent years and compares it with traditional fetal heart monitoring, aiming to provide reference and insights for clinical applications of remote fetal heart monitoring.

Objective To investigate the effective interval of programmed intermittent epidural bolus(PIEB)in labor analgesia with ropivacaine and hydromorphone.Methods A total of 101 puerpera with singleton pregnancy,head position,aged 20-40 years,BMI 20-35 kg/m2,ASA physical status Ⅰ orⅡ,gestational age 37-42 weeks,were randomly allocated into five different PIEB intervals groups:PIEB 40 minutes group(group P40,n=20),PIEB 50 minutes group(group P50,n=19),PIEB 60 minutes group(group P60,n=20),PIEB 70 minutes group(group P70,n=20),and PIEB 80 minutes group(group P80,n=22).The PIEB dose was fixed at 10 ml with the first bolus given 1 hour after the initial manual loading dose with subsequent boluses continued at intervals according to different group allocation.Analgesia was provided using PIEB with 0.1％ropivacaine combined with hydromorphone 15 μg/ml for a total of 12 ml.Based on effective analgesia rate(proportion of women who do not require additional analgesic drugs by the end of the first stage of labor).Probit regression was used to estimate median effective interval(EI50)and 90％effective interval(EI90).VAS pain scores before analgesia,20 minutes,1 hour,2,3,and 4 hours,at full opening of the cervix were recorded.The highest level of sensory blockade and motor blockade(modified Bromage score)were recorded,maternal adverse effects were recorded.Neonatal weight,1 minute and 5 minutes postnatal Apgar scores were recorded.Results Effective analgesia rates were 95.0％,84.2％,70.0％,55.0％,and 50.0％in groups P40,P50,P60,P70,and P80,respec-tively.The EI50 and EI90 were 76.5 minutes(95％CI 69.0-93.9 minutes)and 43.4 minutes(95％CI 17.6-52.7 minutes)analysing by Probit regression.There were no significant differences in VAS pain score at different time,maximum sensory block plane,modified Bromage score,incidence of maternal adverse re-actions and neonatal conditions between the two groups.Conclusion The EI50 and EI90 of PIEB 0.1％ropiv-acaine combined with hydromorphone 15 μg/ml for labor analgesia were 76.5 minutes(95％CI 69.0-93.9 minutes)and 43.4 minutes(95％CI 17.6-52.7 minutes),respectively.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.

Objective:To explore the health economic evaluation methods of disease screening,providing insights for scholars conducting related research.Methods:By integrating relevant content from modem epidemiology,health statistics,and health economics,it investigates the differences in conducting health economic evaluations of disease screening using authenticity indicators(sensitivity,specificity)and predictive value indicators(positive predictive value,negative predictive value),and combines empirical studies with cases from the literature.Results:Authenticity indicators(sensitivity,specificity)are intrinsic measures of diagnostic tests,unaffected by the disease prevalence in the sample population,making them more suitable for conducting health economic evaluations of disease screening.Moreover,authenticity indicators are easy to obtain,and the model calculations are straightforward and convenient.Case study results demonstrate that compared to risk score screening,the Incremental Cost-Effectiveness Ratio(ICER)of fasting plasma glucose(FPG)testing for type 2 diabetes is 626,indicating that for every additional diabetic patient identified through risk score screening,an extra 626 yuan is spent.Conclusion:It is recommended to use authenticity indicators for conducting health economic evaluations of disease screening,standardize the path methods for model selection,and continuously improve the evidence quality of health economic evaluations of disease screening in China.