Protrusive facial deformities, characterized by the forward displacement of the teeth and/or jaws beyond the normal range, affect a considerable portion of the population. The manifestations and morphological mechanisms of protrusive facial deformities are complex and diverse, requiring orthodontists to possess a high level of theoretical knowledge and practical experience in the relevant orthodontic field. To further optimize the correction of protrusive facial deformities, this consensus proposes that the morphological mechanisms and diagnosis of protrusive facial deformities should be analyzed and judged from multiple dimensions and factors to accurately formulate treatment plans. It emphasizes the use of orthodontic strategies, including jaw growth modification, tooth extraction or non-extraction for anterior teeth retraction, and maxillofacial vertical control. These strategies aim to reduce anterior teeth and lip protrusion, increase chin prominence, harmonize nasolabial and chin-lip relationships, and improve the facial profile of patients with protrusive facial deformities. For severe skeletal protrusive facial deformities, orthodontic-orthognathic combined treatment may be suggested. This consensus summarizes the theoretical knowledge and clinical experience of numerous renowned oral experts nationwide, offering reference strategies for the correction of protrusive facial deformities.
Humans ; Orthodontics, Corrective/methods* ; Consensus ; Malocclusion/therapy* ; Patient Care Planning ; Cephalometry

Objective To explore the risk factors for oral mucosal pressure injuries(OMPI)in patients with endotracheal intubation in the emergency intensive care unit(EICU)and to construct a nomogram prediction model based on these factors.Methods A case-control study design was adopt-ed to retrospectively collect clinical data from 209 adult patients with endotracheal intubation admitted to EICU.The patients were divided into OMPI group(53 patients)and non-OMPI group(156 pa-tients)based on whether OMPI occurred during the observation period.The clinical data of the two groups were analyzed,and multivariate Logistic regression analysis was used to screen risk factors for OMPI in patients with endotracheal intubation in the EICU.R software was used to draw a nomogram prediction model,and the predictive performance of the model was evaluated through the receiver oper-ating characteristic(ROC)curve,calibration curve,and decision curve analysis.Results Statistical-ly significant differences were observed between the two groups in prone position ventilation,vasocon-strictor use,consciousness at the time of intubation,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score at the time of intubation,and duration of endotracheal intubation(P＜0.05).The results of multivariate Logistic regression analysis showed that prone position ventilation(OR=2.545,95％CI,1.261 to 5.135),vasoconstrictor use(OR=1.984,95％CI,1.162 to 3.387),inability to express complaints at time of intubation(OR=3.618,95％CI,1.891 to 6.924),high APACHE 11 score(OR=2.394,95％CI,1.322 to 4.336),and long duration of endotracheal in-tubation(OR=3.995,95％CI,1.857 to 8.593)were all risk factors for OMPI in patients with en-dotracheal intubation in the EICU(P＜0.05).ROC curve analysis showed that the area under the curve of the nomogram prediction model was 0.881;calibration curve analysis showed that the mean absolute error between the predicted probability and the actual probability of the model was 0.016;and decision curve analysis showed that the prediction model had practical value in clinical practice.Conclusion Prone position ventilation,vasoconstrictor use,inability to express complaints at the time of intubation,high APACHE Ⅱ score,and long duration of endotracheal intubation are all risk factors for OMPI in patients with endotracheal intubation in the EICU.The nomogram model con-structed based on these factors has good predictive performance for OMPI risk.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.

With the rise of new quality productive forces,the financial management of public hospitals is facing unprecedented opportunities for transformation.After defining the concept of new quality productive forces,it analyzed the impact of"digital new quality productive forces"represented by digital technologies such as the Internet,big data,cloud computing,artificial intelligence,blockchain,and the Internet of Things on the financial management of public hospitals.Subsequently,it systematically elaborated on the key strategies for the digital transformation of financial management in public hospitals from five aspects:technological innovation,process reengineering,risk management and compliance,talent cultivation,and data-driven decision-making.