INTRODUCTION: Obstructive sleep apnoea (OSA) is common in Singapore, with moderate to severe OSA affecting around 30% of residents. These consensus statements aim to provide scientifically grounded recommendations for the management of OSA, standar-dise the management of OSA in Singapore and promote multidisciplinary collaboration. METHOD: An expert panel, which was convened in 2024, identified several areas of OSA management that require guidance. The expert panel reviewed the current literature and developed consensus statements, which were later independently voted on using a 3-point Likert scale (agree, neutral or disagree). Consensus (total ratings of agree and neutral) was set a priori at ≥80% agreement. Any statement not reaching consensus was excluded. RESULTS: The final consensus included 49 statements that provide guidance on the screening, diagnosis and management of adults with OSA. Additionally, 23 statements on the screening, diagnosis and management of paediatric OSA achieved consensus. These 72 consensus statements considered not only the latest clinical evidence but also the benefits and harms, resource implications, feasibility, acceptability and equity impact of the recommendations. CONCLUSION The statements presented in this paper aim to guide clinicians based on the most updated evidence and collective expert opinion from sleep specialists in Singapore. These recommendations should augment clinical judgement rather than replace it. Management decisions should be individualised, taking into account the patient's clinical characteristics, as well as patient and caregiver concerns and preferences.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Singapore ; Consensus ; Adult

Objective To investigate the efficacy and safety of intensity-modulated radiation therapy combined with camrelizumab in the treatment of advanced hepatocellular carcinoma(HCC).Methods A total of 84 patients with advanced HCC admitted to our hospital from January to December 2020 were selected as the study objects,and were randomly divided into the observation group and the control group,with 42 cases in each group.Patients in the observation group received intensity-modulated radiation therapy combined with carrelli-zumab,and patients in the control group received intensity-modulated radiation therapy.The short-term efficacy,immune function and long-term survival rate of patietns in the two groups were compared,and the incidence of adverse reactions was recorded.Results The total effec-tive rates of the observation group 1 month and 3 months after treatment were significantly higher than those of the control group(P<0.05).The levels of CD3+,CD4+ and CD4+/CD8+ 1 month and 3 months after treatment were all increased in the two groups,while the levels of CD8+ in both two groups were decreased(P<0.05),and the levels of CD3+,CD4+ and CD4+/CD8+ in the observation group were higher than those in the control group(P<0.05),and the levels of CD8+ in the observation group were lower than those in the control group(P<0.05).The median survival time of patients in the observation group was significantly longer than that of patients in the control group(P<0.05).The incidence of cutaneous capillary hyperplasia in the observation group was higher than that in the control group(P<0.001),and there was no significant difference in the incidence of other adverse reactions between the two groups(P>0.05),and all of adverse reactions were grades 1 to 2.Conclusion Intensity-modulated radiation therapy combined with camrelizumab has a good effect on HCC,it can improve the immune function of the body,and control the development of the disease,with good safety.

Medical device imaging data augmentation is a method of expanding existing datasets by generating new data samples,which is of great significance for improving the performance of artificial intelligence(AI)medical device-related models and clinical application effects.However,traditional data augmentation methods are usually limited by the quality,realism,and diversity of generated samples.Denoising diffusion probabilistic model(DDPM)is a generative model based on the noise diffusion process,and its main idea is to generate samples with high quality by modelling the sampling process of the target distribution as a process of progressive denoising from the noise distribution.The basic principles and working mechanisms of DDPM were reviewed,the application scenarios of this method in AI medical device data augmentation were analyzed,and its advantages,challenges,and future development directions were explored to provide a reference for the field of AI medical device data augmentation.

In recent years, artificial intelligence (AI) technology represented by deep learning (DL) has developed rapidly, and smart medical care has become one of the most important application areas of AI. As the most accurate noninvasive test to assess myocardial blood flow, myocardial perfusion imaging (MPI) has important clinical values. At present, the use of DL algorithms to establish learning models for MPI images is still in the research stage, and more external verification and iterative updates are needed before it can be widely used in real time clinical practice. In this article, the application of DL algorithms in MPI is comprehensively elaborated to provide a basis and direction for further research.

Currently， there are many difficulties in formulating recommendations of traditional Chinese medicine （TCM） clinical practice guidelines. This paper analyzed and summarized the unique or prominent difficult issues in the formulation of recommendations faced by TCM guidelines， such as experts' professional background and experience bringing about the preferance from the academic emotion， inconsistency between different academic schools making it difficult to reach consensus， lack of guiding principles of the decision weight of different dimensions for recommendations. Therefore， methodological suggestions have been put forward， including organizing parallel TCM and western medicine consensus group， improving the method of combining TCM and western medicine paradigm， attaching great importance to the evidence-based governance of academic schools， and promoting the research on different dimensions for recommendation formulation， which may provide a methodological reference for the guideline development.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.