This paper outlines the common aspects of constructing integrated urban medical groups,focusing on governance,organizational restructuring,operational modes,and mechanism synergy.It then delves into the challenges in China's group construction,highlighting issues with power-responsibility alignment,capacity evolution,incentive alignment,and performance evaluation.Finally,the paper suggests strategies to enhance China's compact urban medical groups,focusing on governance reform,capacity building,benefit integration,and performance evaluation.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Objective: To construct a prognostic model for unsuccessful removal of nasogastric tube (NGT) was the aim of our study. Methods: This study examined patients with swallowing disorders receiving NGT feeding due to stroke or traumatic brain injury in a regional hospital. Clinical data was collected, such as age, sex, body mass index (BMI), level of activities of daily living (ADLs) dependence. Additionally, gather information regarding the enhancement in Functional Oral Intake Scale (FOIS) levels and the increase in food types according to the International Dysphagia Diet Standardization Initiative (IDDSI) after one month of swallowing training. A stepwise logistic regression analysis model was employed to predict NGT removal failure using these parameters. Results: Out of 203 patients, 53 patients (26.1%) had experienced a failed removal of NGT after six months of follow-up. The strongest predictors for failed removal were age over 60 years, underweight BMI, total dependence in ADLs, and ischemic stroke. The admission prediction model categorized patients into high, moderate, and low-risk groups for removal failure. The failure rate of NGT removal was high not only in the high-risk group but also in the moderate-risk groups when there was no improvement in FOIS levels and IDDSI food types. Conclusion Our predictive model categorizes patients with brain insults into risk groups for swallowing disorders, enabling advanced interventions such as percutaneous endoscopic gastrostomy for high-risk patients struggling with NGT removal, while follow-up assessments using FOIS and IDDSI aid in guiding rehabilitation decisions for those at moderate risk.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Objective: Previous research has explored a variety of mental disorders associated with Internet Gaming Disoder (IGD) and Social Media Addiction (SMA). To date, few studies focused on the network characteristics and investigated mood and sleep symptoms across SMA and IGD of adolescence at a group-specific level. This study aims to identify different characteristics of IGD and SMA and further determine the group-specific psychopathology process among adolescents. Methods: We conducted a cross-sectional study to recruit a cohort of 7,246 adolescents who were scored passing the cutoff point of Internet Gaming Disorder Scale-Short Form and Bergen Social Media Addiction Scale, as grouped in IGD and SMA, or otherwise into the control group. Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-item, and Pittsburgh Sleep Quality Index were assessed for the current study, and all assessed items were investigated using network analysis. Results: Based on the analytical procedure, the participants were divided into three groups, the IGD group (n=789), SMA group (n=713) and control group (n=5,744). The edge weight bootstrapping analysis shows that different groups of networks reach certain accuracy, and the network structures of the three groups are statistically different (pcontrol-IGD=0.004, pcontrol-SMA<0.001, pIGD-SMA<0.001). The core symptom of SMA is “feeling down, depressed, or hopeless”, while IGD is “feeling tired or having little energy”. Conclusion Although IGD and SMA are both subtypes of internet addiction, the psychopathology processes of IGD and SMA are different. When dealing with IGD and SMA, different symptoms should be addressed.

Objective: To evaluate the association between pre-and post-diagnosis body mass index (BMI) and risk of colorectal cancer (CRC) death. Methods: The cohort consisted of 3, 057 CRC patients from Shanghai who were diagnosed from Jan. 1, 2009 to Dec. 31, 2011 and aged from 20 to 74 years. The pre- and post-diagnosis BMI and clinical and lifestyle factors were collected at baseline. Death information was collected using record linkage with the Shanghai Cancer Registry and telephone confirmation during follow-up by the end of 2019. The Cox proportional regression model was used to estimate HR with 95% CI. Results: Analysis by multivariable Cox model showed no association between pre-diagnosis BMI and death risk in both male and female patients. Male patients with a post-diagnosis underweight BMI had an elevated risk of death compared to those in normal weight (HR=1.69, 95% CI: 1.21-2.37), especially in early stage cases. Overweight patients (HR=0.74, 95% CI: 0.61-0.89) and patients with obesity class Ⅰ (HR=0.63, 95% CI: 0.45-0.89)had better survival with decreased risks of death, especially in advanced stage cases. The decreased death risk in patients with obesity class Ⅱ was not significant (HR=0.57, 95% CI: 0.24-1.39). The P(trend) value for decreased risk of death with increased BMI in female patients was statistically significant (P<0.001), and the overweight and obesity class Ⅰ categories had better survival in advanced stage(HR(overweight)=0.62, 95% CI: 0.42-0.93; HR(obesity class Ⅰ)=0.39, 95% CI: 0.16-0.98). Both male and female patients with post-diagnosis BMI loss >2.0 kg/m(2) had an increased death risk when compared with those with stable BMI (change≤1.0 kg/m(2)) between pre- and post-diagnosis. BMI gain after diagnosis did not change death risk. Conclusions: Post-diagnosis BMI in the overweight or obesity class Ⅰ groups might be conducive to prolonging male CRC patients' survival, while underweight might result in poor prognosis. Keeping weight and avoiding excessive weight loss should be suggested for all CRC patients after diagnosis.
Female ; Humans ; Male ; Body Mass Index ; China/epidemiology* ; Colorectal Neoplasms/complications* ; Obesity/complications* ; Overweight/complications* ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; Thinness/complications* ; Young Adult ; Adult ; Middle Aged ; Aged

Functional reference limits describe key changes in the physiological relationship between a pair of physiologically related components. Statistically, this can be represented by a significant change in the curvature of a mathematical function or curve (e.g., an observed plateau). The point at which the statistical relationship changes significantly is the point of curvature inflection and can be mathematically modeled from the relationship between the interrelated biomarkers. Conceptually, they reside between reference intervals, which describe the statistical boundaries of a single biomarker within the reference population, and clinical decision limits that are often linked to the risk of morbidity or mortality and set as thresholds. Functional reference limits provide important physiological and pathophysiological insights that can aid laboratory result interpretation. Laboratory professionals are in a unique position to harness data from laboratory information systems to derive clinically relevant values. Increasing research on and reporting of functional reference limits in the literature will enhance their contribution to laboratory medicine and widen the evidence base used in clinical decision limits, which are currently almost exclusively contributed to by clinical trials. Their inclusion in laboratory reports will enhance the intellectual value of laboratory professionals in clinical care beyond the statistical boundaries of a healthy reference population and pave the way to them being considered in shaping clinical decision limits. This review provides an overview of the concepts related to functional reference limits, clinical examples of their use, and the impetus to include them in laboratory reports.

ObjectiveTo further study the pathogenic role of different types of Chlamydia trachomatis （CT） proteins in tubal factor infertility， evaluate the clinical detection value of Chlamydia trachomatis protein antibody in predicting tubal factor infertility. MethodsA total of 58 cases of tubal factor infertility （TFI）， 41 cases of fertile controls （FC） and 18 cases of infertile controls （IFC） were included. For serum detection， first， CT-IgG ELISA kit was used to detect the expression of CT-IgG in serum of three groups of people； then， 6 kinds of Chlamydia trachomatis proteins were expressed and purified in the early stage to establish the antibody test for these proteins， and ELISA detection method was used to detect the expression of their antibodies in the serum of TFI group， FC group and IFC group， respectively； and finally， the antibody OD value of the 6 kinds of Chlamydia trachomatis proteins in the three groups of subjects were statistically described， and CT-IgG was used as the reference standard to draw the receiver operating characteristic curve （ROC curve） of each CT antibody. The Youden Index determines the cutoff value for each antibody. Taking TFI as the reference class， two disordered multiple classification logistic regression models were established with the FC and IFC groups， respectively； and the reference class was used to explore the value of various antibodies and age in predicting TFI， FC and IFC of Chlamydia trachomatis. The back-off method was used to screen the variables. ResultsThe OD value of CT376 antibody in the TFI group was higher than that in the FC group （0.86 vs. 0.60， P=0.026）. The CT376 antibody OD value in the TFI group was higher than that in the IFC group （0.86 vs. 0.64， P=0.026）. The CT443 antibody OD value in the IFC group was higher than that in the TFI group （0.59 vs. 0.34， P=0.036） and higher than that in the FC group （0.59 vs. 0.30， P=0.02）. The multiple classification logistic regression analysis established between TFI and FC showed that CT-IgG ［P<0.001， OR=0.084， 95%CI （0.025， 0.284）］， CT376 antibody ［P=0.068， OR=0.359， 95%CI （0.120， 1.078）］. CT-IgG is an independent risk factor for tubal infertility， and CT376 antibody cannot be an independent risk factor for tubal infertility. The multiple classification logistic regression analysis established between TFI and IFC showed that among infertile patients， CT-IgG ［P<0.05， OR=0.194， 95%CI （0.046， 0.817）］， CT376 antibody ［P<0.05， OR=0.176， 95%CI （0.038， 0.818）］ and CT381 antibody ［P<0.05， OR=0.112， 95%CI （ 0.016， 0.796）］ were independent risk factors for tubal infertility. ConclusionThe expression of CT376 antibody in tubal infertility patients is higher than that in fertile and infertile controls， suggesting that CT-induced tubal factor infertility may be related to CT376. CT-IgG， and CT376 antibodies are meaningful in predicting CT-induced tubal factor infertility.

INTRODUCTION: We investigated the knowledge, attitudes and practice (KAP) towards coronavirus disease 2019 (COVID-19) and its related preventive measures in Singaporeans aged ≥60 years. METHODS: This was a population-based, cross-sectional, mixed-methods study (13 May 2020-9 June 2020) of participants aged ≥ 60 years. Self-reported KAP about ten symptoms and six government-endorsed preventive measures related to COVID-19 were evaluated. Multivariable regression models were used to identify sociodemographic and health-related factors associated with KAP in our sample. Associations between knowledge/attitude scores and practice categories were determined using logistic regression. Seventy-eight participants were interviewed qualitatively about the practice of additional preventive measures and data were analysed thematically. RESULTS: Mean awareness score of COVID-19 symptoms was 7.2/10. The most known symptom was fever (93.0%) and the least known was diarrhoea (33.5%). Most participants knew all six preventive measures (90.4%), perceived them as effective (78.7%) and practised 'wear a mask' (97.2%). Indians, Malays and participants living in smaller housing had poorer mean scores for knowledge of COVID-19 symptoms. Older participants had poorer attitudes towards preventive measures. Compared to Chinese, Indians had lower odds of practising three out of six recommendations. A one-point increase in score for knowledge and attitudes regarding preventive measures resulted in higher odds of always practising three of six and two of six measures, respectively. Qualitative interviews revealed use of other preventive measures, for example, maintaining a healthy lifestyle. CONCLUSIONS Elderly Singaporeans displayed high levels of KAP about COVID-19 and its related preventive measures, with a positive association between levels of knowledge/attitude and practice. However, important ethnic and socioeconomic disparities were evident, indicating that key vulnerabilities remain, which require immediate attention.
Humans ; Aged ; COVID-19/epidemiology* ; SARS-CoV-2 ; Health Knowledge, Attitudes, Practice ; Cross-Sectional Studies ; Singapore/epidemiology* ; Surveys and Questionnaires

Objective: To evaluate the association between pre-and post-diagnosis body mass index (BMI) and risk of colorectal cancer (CRC) death. Methods: The cohort consisted of 3, 057 CRC patients from Shanghai who were diagnosed from Jan. 1, 2009 to Dec. 31, 2011 and aged from 20 to 74 years. The pre- and post-diagnosis BMI and clinical and lifestyle factors were collected at baseline. Death information was collected using record linkage with the Shanghai Cancer Registry and telephone confirmation during follow-up by the end of 2019. The Cox proportional regression model was used to estimate HR with 95% CI. Results: Analysis by multivariable Cox model showed no association between pre-diagnosis BMI and death risk in both male and female patients. Male patients with a post-diagnosis underweight BMI had an elevated risk of death compared to those in normal weight (HR=1.69, 95% CI: 1.21-2.37), especially in early stage cases. Overweight patients (HR=0.74, 95% CI: 0.61-0.89) and patients with obesity class Ⅰ (HR=0.63, 95% CI: 0.45-0.89)had better survival with decreased risks of death, especially in advanced stage cases. The decreased death risk in patients with obesity class Ⅱ was not significant (HR=0.57, 95% CI: 0.24-1.39). The P(trend) value for decreased risk of death with increased BMI in female patients was statistically significant (P<0.001), and the overweight and obesity class Ⅰ categories had better survival in advanced stage(HR(overweight)=0.62, 95% CI: 0.42-0.93; HR(obesity class Ⅰ)=0.39, 95% CI: 0.16-0.98). Both male and female patients with post-diagnosis BMI loss >2.0 kg/m(2) had an increased death risk when compared with those with stable BMI (change≤1.0 kg/m(2)) between pre- and post-diagnosis. BMI gain after diagnosis did not change death risk. Conclusions: Post-diagnosis BMI in the overweight or obesity class Ⅰ groups might be conducive to prolonging male CRC patients' survival, while underweight might result in poor prognosis. Keeping weight and avoiding excessive weight loss should be suggested for all CRC patients after diagnosis.
Female ; Humans ; Male ; Body Mass Index ; China/epidemiology* ; Colorectal Neoplasms/complications* ; Obesity/complications* ; Overweight/complications* ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; Thinness/complications* ; Young Adult ; Adult ; Middle Aged ; Aged