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.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To analyze the effects of the disease and injury spectrum on health-adjusted life expectancy (HALE) among permanent residents aged 55 and above in Shenzhen from 2016 to 2030.Methods:Based on the mortality surveillance data and the permanent resident population data in Shenzhen from 2016 to 2022, the Sullivan method was used to calculate the HALE during 2016—2022. The Bayesian age-period-cohort model and the grey system model were used to predict the HALE during 2023—2030. The HALE changes in the two periods were decomposed into the contributions of 20 categories of diseases and injuries, respectively.Results:From 2016 to 2022, the HALE increased from 31.41 years (95% CI: 30.50-32.32) to 33.57 years (95% CI: 32.47-34.67). During this period, the mortality effect of neurological disorders slowed the increase of HALE, with a reduction of 0.27 years. By 2030, it is anticipated that the HALE will reach 36.40 years (95% CI: 34.78-38.01). This is expected to be influenced by the mortality effects of nutritional deficiencies (-0.40 years) and mental disorders (-0.29 years), as well as the disability effects of musculoskeletal disorders (-0.66 years), skin and subcutaneous diseases (-0.21 years) and nutritional deficiencies (-0.13 years). Conclusion:The HALE of permanent residents aged 55 years and above in Shenzhen demonstrated an increasing trend over time. Greater attention should be paid to the adverse effects of neurological disorders, nutritional deficiencies, mental disorders, musculoskeletal disorders, and skin and subcutaneous diseases on the continuous increase of HALE in this population.

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.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To analyze the effects of the disease and injury spectrum on health-adjusted life expectancy (HALE) among permanent residents aged 55 and above in Shenzhen from 2016 to 2030.Methods:Based on the mortality surveillance data and the permanent resident population data in Shenzhen from 2016 to 2022, the Sullivan method was used to calculate the HALE during 2016—2022. The Bayesian age-period-cohort model and the grey system model were used to predict the HALE during 2023—2030. The HALE changes in the two periods were decomposed into the contributions of 20 categories of diseases and injuries, respectively.Results:From 2016 to 2022, the HALE increased from 31.41 years (95% CI: 30.50-32.32) to 33.57 years (95% CI: 32.47-34.67). During this period, the mortality effect of neurological disorders slowed the increase of HALE, with a reduction of 0.27 years. By 2030, it is anticipated that the HALE will reach 36.40 years (95% CI: 34.78-38.01). This is expected to be influenced by the mortality effects of nutritional deficiencies (-0.40 years) and mental disorders (-0.29 years), as well as the disability effects of musculoskeletal disorders (-0.66 years), skin and subcutaneous diseases (-0.21 years) and nutritional deficiencies (-0.13 years). Conclusion:The HALE of permanent residents aged 55 years and above in Shenzhen demonstrated an increasing trend over time. Greater attention should be paid to the adverse effects of neurological disorders, nutritional deficiencies, mental disorders, musculoskeletal disorders, and skin and subcutaneous diseases on the continuous increase of HALE in this population.

With the increasing life expectancy and lifestyle changes of patients with chronic hepatitis B (CHB), the significance of comorbidities of chronic non-communicable diseases (NCDs) in disease progression and health prognosis of CHB patients is gaining prominence. This study aims to explore the association between CHB and NCDs comorbidities, focusing on the impact of common metabolism-related diseases, such as metabolic syndrome and diabetes, on the health outcomes of CHB patients. We also summarize studies on integrating the management of comorbidities in CHB patients and provide relevant recommendations for effective management. The findings of this study serve as a foundation for understanding the clinical characteristics and prevalence trends, reducing the disease burden of comorbidities among CHB patients, and establishing a comprehensive and coordinated management system for comorbidities.

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.