Objective:To develop a training curriculum targeting the core competences of neurosurgery nurses, so as to provide a reference for standardized training of neurosurgery nurses and to strengthen their professionality.Methods:Centering on the core competences required for neurosurgery nurses, we formed the curriculum framework based on the cell structure-inspired concept proposed by the School of Nursing of The Hong Kong Polytechnic University, and determined the contents through a literature analysis and two rounds of questionnaire-based Delphi consultations with 16 experts. We increased, reduced, or modified the items and dimensions, and finally estabilished the curriculum for neurosurgery nurse training. SPSS 18.0 software was used to analyze the general information of experts, coordination coefficient, variation coefficient, full mark ratio, and mean importance value. Categorical data were described as frequencies and percentages, and continuous data were described as mean±standard deviation. Ranked data were analyzed by using the chi-square test.Results:In the two rounds of expert consultations, questionnaires were both 100.00% returned, and the coefficients of expert authority were 0.873 and 0.888, respectively. The coordination coefficient of curriculum modules and curriculum contents were 0.416 and 0.221, respectively ( P<0.001). For individual items across the curriculum modules, the mean importance value was 3.188-3.938, the variation coefficient was 0.063-0.178, and the full mark ratio was 0.250-0.875. For content items, the mean importance value was 3.250-4.000, the variation coefficient was 0-0.238, and the full mark ratio was 0.375-1.000. The established clinical practice-directed curriculum for neurosurgery nursing training included 6 course modules (anatomy and physiology of the nervous system, neurosurgery patient evaluation and monitoring, neurosurgery disease nursing, commonly used neurosurgery nursing techniques, nursing management, and scientific research basis) and 77 content items. Conclusions:The established training curriculum targeting the core competences for neurosurgery nurses is reliable, which can provide a reference for standardized training of neurosurgery nurses.

Infectious diseases of the respiratory system are very common in clinical practice,among which pneumonia caused by in-fluenza virus infection is characterized by high morbidity and mortality.Current clinical treatments for respiratory viral infections still face challenges such as drug resistance and poor efficacy.Necroptosis,a form of programmed cell death,is closely associated with the progression of respiratory viral infections.Traditional Chinese medicine modulating necroptosis has demonstrated beneficial therapeu-tic effects in these diseases.This paper introduces the concept and mechanisms of necroptosis,reviews recent research on the relation-ship between necroptosis and respiratory virus infections diseases,and summarizes the therapeutic effects of traditional Chinese medi-cine in regulating necroptosis to intervene in these diseases,aiming to provide insights for the development and clinical application of therapeutic agents for respiratory viral infections.

Objective:To construct and validate the risk prediction model of osteoporosis (OP) in the middle-aged and elderly healthy physical examination population.Methods:In this cross-sectional study, 18 030 middle-aged and elderly people with bone mineral density tested in Health Management Center of Guangxi Zhuang Autonomous Region Hospital from January 2020 to December 2022 were selected. The general data, physical examination index and biochemical blood index were collected. The subjects were divided into training set (12 621 cases) and validation set (5 409 cases) in a ratio of 7∶3 with the simple random sampling method. The variables were screened with minimum LASSO regression and logistic regression and the corresponding nomogram prediction model for the risk of osteoporosis in the middle-aged and elderly health examination population was established. The performance of the nomogram model was evaluated with the area under the receiver operating characteristic curve (ROC AUC), specificity, sensitivity, calibration curve (CAL), and decision curve (DCA).Results:The results of LASSO regression and multivariate logistic regression in training set showed that gender, age, body mass index, hip circumference, waist circumference, systolic blood pressure, total cholesterol, glutamyl transpeptidase and albumin/globulin ratio were the independent best predictors of OP risk in the middle-aged and elderly health examination population (all P<0.05). The ROC AUC-value of the training set was 0.895 (95% CI: 0.886-0.904), with a sensitivity of 87.25% and a specificity of 85.01%. The ROC AUC value of the validation set was 0.892 (95% CI: 0.886-0.898), with a sensitivity of 83.74% and a specificity of 82.46%. The CAL showed a C-index value of 0.790 in the training set and a C-index value of 0.784 in validation set. The CALs all showed deviation correction and obvious curves similar to the ideal line. DCA showed that when the OP risk threshold probability of the training set was 45%-93%, and the OP risk threshold probability of the validation set was 45%-92%, the nomogram model had better efficacy in predicting OP risk in the middle-aged and elderly physical examination population, and the two results were still relatively consistent. Both CAL and DCA showed good performance. Conclusion:This study establishes a practical prediction model for osteoporosis risk in the middle-aged and elderly population, it can provide an early warning for the timely detection of OP risk for the middle-aged and elderly people.

Objective To construct a classification model for surgical patients and apply it in the allocation of nursing human resources,providing a reference for nursing human resource management.Methods A convenience sampling method was used to retrospectively select 5,431 hospitalized surgical patients admitted to 6 surgical nursing units of a tertiary general hospital in Suzhou from July to November 2022 as the subjects of this study.The nursing hours were measured,and related influencing factors were analyzed.A decision tree classification method was used to establish a classification model for surgical patients.From August to October 2022,1,527 hospitalized surgical patients admitted to 3 nursing units of the same hospital were conveniently selected.The minimum number of nurses required daily was calculated using the surgical patient classification model,actual nursing hours measurement method,nurse-to-bed ratio method,and 8-hour continuous shift scheduling method.The application effect of the surgical patient classification model in nursing human resource allocation was evaluated with the actual nursing hours measurement method as the standard.Results The surgical patient classification model includes 7 classification indicators:length of hospital stay,diagnosis-related group weight,presence or absence of secondary care orders,surgical grade,anesthesia method,age,and presence or absence of critical illness orders.Patients were divided into 14 groups,and the model explained 90.5％of the total variance in nursing workload.The minimum number of nurses required in surgical nursing units calculated based on this model was closest to the result of the actual nursing hours measurement method and was superior to the results of the nurse-to-bed ratio method and the 8-hour continuous shift scheduling method.Conclusion The surgical patient classification model can accurately reflect the nursing workload of such patients.The classification indicators are simple and easy to obtain,and can guide the allocation of human resources in surgical nursing units.

Objective:To develop a training curriculum targeting the core competences of neurosurgery nurses, so as to provide a reference for standardized training of neurosurgery nurses and to strengthen their professionality.Methods:Centering on the core competences required for neurosurgery nurses, we formed the curriculum framework based on the cell structure-inspired concept proposed by the School of Nursing of The Hong Kong Polytechnic University, and determined the contents through a literature analysis and two rounds of questionnaire-based Delphi consultations with 16 experts. We increased, reduced, or modified the items and dimensions, and finally estabilished the curriculum for neurosurgery nurse training. SPSS 18.0 software was used to analyze the general information of experts, coordination coefficient, variation coefficient, full mark ratio, and mean importance value. Categorical data were described as frequencies and percentages, and continuous data were described as mean±standard deviation. Ranked data were analyzed by using the chi-square test.Results:In the two rounds of expert consultations, questionnaires were both 100.00% returned, and the coefficients of expert authority were 0.873 and 0.888, respectively. The coordination coefficient of curriculum modules and curriculum contents were 0.416 and 0.221, respectively ( P<0.001). For individual items across the curriculum modules, the mean importance value was 3.188-3.938, the variation coefficient was 0.063-0.178, and the full mark ratio was 0.250-0.875. For content items, the mean importance value was 3.250-4.000, the variation coefficient was 0-0.238, and the full mark ratio was 0.375-1.000. The established clinical practice-directed curriculum for neurosurgery nursing training included 6 course modules (anatomy and physiology of the nervous system, neurosurgery patient evaluation and monitoring, neurosurgery disease nursing, commonly used neurosurgery nursing techniques, nursing management, and scientific research basis) and 77 content items. Conclusions:The established training curriculum targeting the core competences for neurosurgery nurses is reliable, which can provide a reference for standardized training of neurosurgery nurses.

Objective To investigate the status of perceived uncertainty in the process of re-pregnancy in patients with recurrent abortion,and to systematically explore the influencing factors based on the health ecology model,to inform a reduction in the level of perceived uncertainty about their re-pregnancy and improve their quality of life.Methods A convenience sampling method was used to investigate 260 patients with recurrent spontaneous abortion during re-pregnancy from 2 reproductive clinics of a tertiary A hospital in Suzhou,from December 2023 to June 2024,and they were investigated by a self-designed general data questionnaire,Intolerance of Uncertainty Scale-12,Generalized Anxiety Disorder Scale and Simple Coping Style Questionnaire.The single factor analysis and multiple linear regression methods were used to analyze the influences factors of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy.Results A total of 256 valid questionnaires were collected,with a recovery rate of 98.5％.The score of intolerance of uncertainty in patients with recurrent spontaneous abortion during re-pregnancy was(35.18±9.23).Multiple linear regression analysis found that average monthly family income,residence,history with assisted reproductive therapy,anxiety and coping were the factors influencing the feeling of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy(P＜0.05).Conclusion The level of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy is at a medium level.Medical workers should pay attention to the early identification of the perceived uncertainty in patients,enhance the psychological support for patients,and improve their mental health.

Objective To investigate the status of perceived uncertainty in the process of re-pregnancy in patients with recurrent abortion,and to systematically explore the influencing factors based on the health ecology model,to inform a reduction in the level of perceived uncertainty about their re-pregnancy and improve their quality of life.Methods A convenience sampling method was used to investigate 260 patients with recurrent spontaneous abortion during re-pregnancy from 2 reproductive clinics of a tertiary A hospital in Suzhou,from December 2023 to June 2024,and they were investigated by a self-designed general data questionnaire,Intolerance of Uncertainty Scale-12,Generalized Anxiety Disorder Scale and Simple Coping Style Questionnaire.The single factor analysis and multiple linear regression methods were used to analyze the influences factors of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy.Results A total of 256 valid questionnaires were collected,with a recovery rate of 98.5％.The score of intolerance of uncertainty in patients with recurrent spontaneous abortion during re-pregnancy was(35.18±9.23).Multiple linear regression analysis found that average monthly family income,residence,history with assisted reproductive therapy,anxiety and coping were the factors influencing the feeling of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy(P＜0.05).Conclusion The level of perceived uncertainty in patients with recurrent spontaneous abortion during re-pregnancy is at a medium level.Medical workers should pay attention to the early identification of the perceived uncertainty in patients,enhance the psychological support for patients,and improve their mental health.

Objective:To construct and validate the risk prediction model of osteoporosis (OP) in the middle-aged and elderly healthy physical examination population.Methods:In this cross-sectional study, 18 030 middle-aged and elderly people with bone mineral density tested in Health Management Center of Guangxi Zhuang Autonomous Region Hospital from January 2020 to December 2022 were selected. The general data, physical examination index and biochemical blood index were collected. The subjects were divided into training set (12 621 cases) and validation set (5 409 cases) in a ratio of 7∶3 with the simple random sampling method. The variables were screened with minimum LASSO regression and logistic regression and the corresponding nomogram prediction model for the risk of osteoporosis in the middle-aged and elderly health examination population was established. The performance of the nomogram model was evaluated with the area under the receiver operating characteristic curve (ROC AUC), specificity, sensitivity, calibration curve (CAL), and decision curve (DCA).Results:The results of LASSO regression and multivariate logistic regression in training set showed that gender, age, body mass index, hip circumference, waist circumference, systolic blood pressure, total cholesterol, glutamyl transpeptidase and albumin/globulin ratio were the independent best predictors of OP risk in the middle-aged and elderly health examination population (all P<0.05). The ROC AUC-value of the training set was 0.895 (95% CI: 0.886-0.904), with a sensitivity of 87.25% and a specificity of 85.01%. The ROC AUC value of the validation set was 0.892 (95% CI: 0.886-0.898), with a sensitivity of 83.74% and a specificity of 82.46%. The CAL showed a C-index value of 0.790 in the training set and a C-index value of 0.784 in validation set. The CALs all showed deviation correction and obvious curves similar to the ideal line. DCA showed that when the OP risk threshold probability of the training set was 45%-93%, and the OP risk threshold probability of the validation set was 45%-92%, the nomogram model had better efficacy in predicting OP risk in the middle-aged and elderly physical examination population, and the two results were still relatively consistent. Both CAL and DCA showed good performance. Conclusion:This study establishes a practical prediction model for osteoporosis risk in the middle-aged and elderly population, it can provide an early warning for the timely detection of OP risk for the middle-aged and elderly people.

This consensus was developed by the Orthodontic Society of the Chinese Stomatological Association to provide a systematic, scientific, and practical guideline for informed consent in orthodontic care. Orthodontic treatment is typically lengthy, highly individualized, and involves multiple factors such as growth and development, occlusal function, and facial esthetics. Rapid technological advances and diverse risk profiles make the traditional reliance on orthodontist experience or institutional templates insufficient to ensure patients′ full understanding and autonomous decision-making. To address this, the expert panel conducted extensive reviews of domestic and international guidelines, analyzed representative dispute cases, and performed multicenter patient-clinician surveys. Using a multi-round Delphi method, the group established a standardized informed consent framework covering the initial consultation, treatment, and retention phases. The consensus emphasizes that informed consent is not only a fundamental legal and ethical requirement but also a key step in building trust, improving patient compliance, and enhancing treatment satisfaction. Orthodontists should clearly and comprehensively explain treatment plans, potential risks, uncertainties, and associated costs, while respecting the autonomy of patients or guardians, and maintain continuous communication and dynamic evaluation throughout the treatment process. The release of this consensus provides unified and authoritative guidance for clinical orthodontics, helping to standardize informed consent, enhance its transparency, safeguard patient rights, reduce medical risks, and promote high-quality, sustainable development of orthodontic practice.

Objective To construct a classification model for surgical patients and apply it in the allocation of nursing human resources,providing a reference for nursing human resource management.Methods A convenience sampling method was used to retrospectively select 5,431 hospitalized surgical patients admitted to 6 surgical nursing units of a tertiary general hospital in Suzhou from July to November 2022 as the subjects of this study.The nursing hours were measured,and related influencing factors were analyzed.A decision tree classification method was used to establish a classification model for surgical patients.From August to October 2022,1,527 hospitalized surgical patients admitted to 3 nursing units of the same hospital were conveniently selected.The minimum number of nurses required daily was calculated using the surgical patient classification model,actual nursing hours measurement method,nurse-to-bed ratio method,and 8-hour continuous shift scheduling method.The application effect of the surgical patient classification model in nursing human resource allocation was evaluated with the actual nursing hours measurement method as the standard.Results The surgical patient classification model includes 7 classification indicators:length of hospital stay,diagnosis-related group weight,presence or absence of secondary care orders,surgical grade,anesthesia method,age,and presence or absence of critical illness orders.Patients were divided into 14 groups,and the model explained 90.5％of the total variance in nursing workload.The minimum number of nurses required in surgical nursing units calculated based on this model was closest to the result of the actual nursing hours measurement method and was superior to the results of the nurse-to-bed ratio method and the 8-hour continuous shift scheduling method.Conclusion The surgical patient classification model can accurately reflect the nursing workload of such patients.The classification indicators are simple and easy to obtain,and can guide the allocation of human resources in surgical nursing units.