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ObjectThis study was designed to analyze the cartilaginous predictors of residual acetabular dysplasia (RAD) after early treatment of developmental dysplasia of the hip and their diagnostic accuracy.Study designDatabases such as PubMed, Embase, Cochrane, and Web of science were searched to screen the literature. The quality of the literature was assessed by the QUADAS-2 tool. Qualitative and quantitative synthesis of literature were performed based on extracted data. For quantitative synthesis studies, the sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve with corresponding confidence intervals were calculated.ResultsFor the cartilaginous acetabular index (CAI) group, the combined values of sensitivity, specificity, and DOR were 0.80 (95% CI = 0.54–0.93), 0.73 (95% CI = 0.57–0.84), and 10.62 (95% CI = 3.96–28.53), respectively. The corresponding values in the cartilaginous center-edge angle (CCE) group were 0.71 (95% CI = 0.57–0.82), 0.78 (95% CI = 0.66–0.87), and 8.64 (95% CI = 3.08–24.25), respectively. The area under the curve (AUC) of SROC was 0.82 (95% CI = 0.78–0.85) and 0.80 (95% CI = 0.76–0.83) for the CAI and CCE groups. The CAI group had higher sensitivity, DOR, and AUC than the CCE group.ConclusionBoth of these two groups have good diagnostic accuracy, and CAI/L-AI has a little edge over CCE/L-CEA. However, there is still more research needed to determine whether they can be used as independent indications for secondary orthopedic surgery.Systematic review registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier: [CRD42022338332].

Global warming has become a matter of concern as maximum temperatures in many parts of the world continue to set new historical records. Based on the global monthly average temperature data from 1750 to 2022, we use the gray forecast, ARIMA, and BP neural network model, respectively, to forecast the global temperature trend and compare the forecast results of different models. Besides, we use the Analytic Hierarchy Process to analyze the main influencing factors of the intensification of climate change and discuss the effects of different factors on global warming. We find that the temperature outlier for March 2022 is higher than any previous data, and the BP neural network model has the best fitting effect on global temperature change prediction. Human factors and some episodic natural factors impact global temperature change, and human factors may be the main cause of global warming. The results reveal that governments should pay attention to abnormal climate change and take measures to curb the role of human activities in promoting global warming. Otherwise, it could be detrimental to future sustainable development. [ABSTRACT FROM AUTHOR]