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Meta-analysis with fixed-effects and random-effects models provides a general framework for quantitatively summarizing multiple comparative studies. However, a majority of the conventional methods rely on large-sample approximations to justify their inference, which may be invalid and lead to erroneous conclusions, especially when the number of studies is not large, or sample sizes of the individual studies are small. In this article, we propose a set of 'exact' confidence intervals for the overall effect, where the coverage probabilities of the intervals can always be achieved. We start with conventional parametric fixed-effects and random-effects models, and then extend the exact methods beyond the commonly postulated Gaussian assumptions. Efficient numerical algorithms for implementing the proposed methods are developed. We also conduct simulation studies to compare the performance of our proposal to existing methods, indicating our proposed procedures are better in terms of coverage level and robustness. The new proposals are then illustrated with the data from meta-analyses for estimating the efficacy of statins and BCG vaccination.

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Background: Patients with inflammatory bowel disease (IBD) often have low weight, malnutrition and sarcopenia. The criteria of sarcopenia used were European and American standards previously. The aim of the study was to evaluate the impact of sarcopenia on clinical outcomes in patients with IBD using the Asian Working Group for Sarcopenia 2019 (AWGS2019) criteria. Methods:The inclusion of the subjects was IBD patients between 18 to 60 years. Sarcopenia, presarcopenia and sarcopenic obesity were defined. Participants were followed up for 90 days. Information as to whether the symptoms improved, treatment plans changed, underwent surgery, were readmitted to the hospital, or died was recorded. Analyses of chi-square test, t-test, cumulative survival analysis and receiver operating characteristic (ROC) curves were done through SPSS25.0 software. Odds ratio (OR) and 95% confidence interval (CI) were calculated.Results: A total of 110 patients with IBD were included. The prevalence of pre-sarcopenia was 44.6% and of sarcopenia 50.8%. Body mass index (BMI) (P=0.018; OR =0.449) and albumin (Alb) levels were lower (P=0.004; OR =0.608) in the sarcopenia group than the control and pre-sarcopenia groups, and they were risk factors for sarcopenia. Meanwhile, a history of more frequent alcohol consumption, parenteral manifestations, IBD-related complications, higher C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were significant statistic different for sarcopenia group compared with others. Rates of surgery (P<0.001; OR =6.651), re-hospitalization (P<0.001; OR =6.344) or death (P=0.003) were higher in the sarcopenia group than in the control group. The sarcopenia group had higher rates of surgery (P=0.022; OR =3.608) and re-hospitalization (P=0.048; OR =5.500) than the pre-sarcopenia group after adjustment analysis.Patients in the sarcopenic obesity group with body fat percentages ≥24.8% (P=0.039; 95% CI: 0.590-1.000) in men and ≥32.0% (P=0.006; 95% CI: 0.692-1.000) in women were more likely to receive surgery, female patients with that ≥24.5% (P=0.025; 95% CI: 0.556-1.000) were more likely to experience re-hospitalization.Conclusions: Patients with IBD diagnosed with sarcopenia or sarcopenic obesity based on AWGS2019 criteria had poorer outcomes. The AWGS2019 criteria are comprehensive and more suitable for predicting outcomes in IBD patients, which helps doctors making precise treatment.

Effective overload control for large-scale online service system is crucial for protecting the system backend from overload. Conventionally, the design of overload control is ad-hoc for individual service. However, service-specific overload control could be detrimental to the overall system due to intricate service dependencies or flawed implementation of service. Service developers usually have difficulty to accurately estimate the dynamics of actual workload during the development of service. Therefore, it is essential to decouple the overload control from service logic. In this paper, we propose DAGOR, an overload control scheme designed for the account-oriented microservice architecture. DAGOR is service agnostic and system-centric. It manages overload at the microservice granule such that each microservice monitors its load status in real time and triggers load shedding in a collaborative manner among its relevant services when overload is detected. DAGOR has been used in the WeChat backend for five years. Experimental results show that DAGOR can benefit high success rate of service even when the system is experiencing overload, while ensuring fairness in the overload control. KEYWORDSoverload control, service admission control, microservice architecture, WeChat * Merged the errata published in https://arxiv.org/abs/1806.04075v2.