Background The relationship between dental caries and periodontal disease is still controversial. The objective of this study was to explore the relationship between different types of caries and periodontal disease severity in middle-aged and elderly people in China. Methods The study population consisted of 4407 middle-aged and 4117 elderly subjects. Caries were divided into the following three types: type A, crown caries; type B, lesions involving both the crown and root, representing mixed-type caries; and type C, root caries. These three types together represent the overall caries situation, which we call type ABC. Caries were quantitated by decayed and filled teeth (DFT index). Periodontitis was evaluated by clinical attachment loss. Results Middle-aged people with periodontitis had a significant association with DFT type B (OR: 1.21, 95% CI 1.17–1.25) and type C (OR: 1.40, 95% CI 1.24–1.56). Elderly people with periodontitis had a significant association with DFT type C (OR: 1.28, 95% CI 1.21–1.35). Conclusions In China, caries types B/C were positively correlated with periodontitis in the middle-aged group, and only caries type C was positively correlated with periodontitis in the elderly group.
Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients. The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China. A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation. The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia. Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.
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