Objective To explore the dietary nutrition status and nutritional intervention strategy of patients with Alzheimer’s disease (AD). Methods Among the 1 332 patients with AD diagnosed at Xijing Hospital from January 2021 to December 2023 were enrolled as the study subjects. The dietary intake data of patients were collected through questionnaire surveys and dietary reviews. During the study period, 30 patients did not complete the intervention due to withdrawal or loss of follow-up. Based on the actual number of people who completed the intervention, AD patients were randomly divided into intervention group (n=651, individualized nutritional intervention strategy) and control group (n=651, routine nutritional intervention), and both groups were intervened for 3 months. The cognitive function (MMSE score and MoCA score), nutritional status (MNA scale, NRS-2002 scale), and quality of life (GQOL-74) of the two groups of AD patients were compared to evaluate the effectiveness of the intervention strategies. Results A total of 1 332 questionnaires were distributed, and 1 302 valid questionnaires were finally recovered, with an effective recovery rate of 97.75% (1 302/1 332). The survey results showed that there were no statistical differences in baseline characteristics and dietary nutrition status between the two groups of AD patients before intervention (P>0.05). After nutritional intervention, the cognitive function, quality of life, and nutritional status of patients in the intervention group were significantly improved. The MMSE score, MoCA score, MNA score, and GQOL-74 score of the intervention group were significantly higher than those of the control group, while the NRS-2002 score was lower than that of the control group (P<0.05). Conclusion Nutritional intervention strategy has a significant effect on improving nutritional status, cognitive function, and quality of life of AD patients.

AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Standardized reporting is a crucial factor affecting the use of patient guidelines （PGs）， particularly in the reporting and presentation of recommendations. This paper introduced the current status of PG reporting， including the research on PG content and presentation formats， and provided comprehensive recommendations for PG reporting from aspects such as overall framework， recommendations， presentation format， and readability. First， the presentation of PG recommendations should include clearly defined clinical questions， recommendations and their rationale， and guidance on how patients should implement the interventions； for specific content in the PG， such as level of evidence， level of recommendation， it is recommended to explain in text the reasons for giving different levels of recommendation， i.e.， to present the logic behind giving the level of recommendation to the patient； additional information needed in the recommendation framework should be supplemented by tracing references or authoritative textbooks and literature that support the recommendations. Subsequently， the PG text should be written based on the Reporting Checklist for Public Versions of Guidelines （RIGHT-PVG） reporting framework. Finally， to enhance readability and comprehension， it is recommended to refer to the Patient Education Materials Assessment Tool （PEMAT） for translating PG content. To enhance the readability of PGs， it is suggested to present the PG content in a persona-lized and layered manner.

Since the concept of patient versions of guidelines （PVGs） was introduced into China， several PVGs have been published in China， but we found that there is a big difference between the concept of PVG at home and abroad， and the reason for this difference has not been reasonably explained， which has led to ambiguity and even misapplication of the PVG concept by guideline developers. By analyzing the background and purpose of PVGs， and the understanding of the PVG concept by domestic scholars， we proposed the term patient guidelines （PGs）. This refers to guidelines developed under the principles of evidence-based medicine， centered on health issues that concern patients， and based on the best available evidence， intended for patient use. Except for the general attribute of providing information or education， which is typical of common health education materials， PGs also provide recommendations and assist in decision-making， so PGs include both the patient versions of guidelines （PVG） as defined by the Guidelines International Network （GIN） and "patient-directed guidelines"， i.e. clinical practice guidelines resulting from the adaptation or reformulation of recommendations through clinical practice guidelines.

At present， the process and methodology of patient guidelines （PGs） development varies greatly and lacks systematic and standardised guidance. In addition to the interviews with PG developers， we have sorted out the relevant methodology for the adaptation and development of existing clinical practice guideline recommendations and facilitated expert deliberations to achieve a consensus， so as to finally put forward a proposal for guidance on the process and methodology for the development of PGs. The development of PGs can be divided into the preparation stage， the construction stage， and the completion stage in general， but the specific steps vary according to the different modes of development of PGs. The development process of Model 1 is basically the same as the patient version of the guideline development process provided by the International Guidelines Network， i.e.， team formation， screening of recommendations， guideline drafing， user testing and feedback， approval and dissemination. The developer should also first determine the need for and scope of translating the clinical practice guideline into a patient version during the preparation phase. Model 2 adds user experience and feedback to the conventional clinical practice guideline development process （forming a team， determining the scope of the PG， searching， evaluating and integrating evidence， forming recommendations， writing the guideline， and expert review）. Based on the different models， we sort out the process and methods of PG development and introduce the specific methods of PG development， including how to identify the clinical problem and how to form recommendations based on the existing clinical practice guidelines， with a view to providing reference for guideline developers and related researchers.