目的:观察研究一种纳米复合陶瓷修复儿童第一磨牙大面积牙体缺损的临床效果。方法:针对临床上29例年龄7~15岁,第一磨牙大面积牙体缺损的患者(共35颗),使用CAD/CAM纳米复合陶瓷进行修复,13颗嵌体和22颗高嵌体。修复后24个月内分别对患者...目的:观察研究一种纳米复合陶瓷修复儿童第一磨牙大面积牙体缺损的临床效果。方法:针对临床上29例年龄7~15岁,第一磨牙大面积牙体缺损的患者(共35颗),使用CAD/CAM纳米复合陶瓷进行修复,13颗嵌体和22颗高嵌体。修复后24个月内分别对患者进行修复体形状、色泽、舒适性的满意度调查,使用改良的美国公共卫生服务(United States Public Health Service,USPHS)标准分别评价修复后即刻、12个月和24个月时的临床疗效,评价项目包括修复体完整性、继发龋、边缘适合性、牙龈健康状况和磨耗程度。结果:35颗患牙修复后即刻、24个月修复体形状和舒适性主观评价满意度均达100%,24个月时色泽满意度下降为91.4%。修复体完整性、继发龋以及牙龈健康状况24个月内均100%达到A级。边缘适合性12个月时均达到A级,24个月时88.6%(31例)达到A级,有统计学差异。磨耗程度上,12个月、24个月的临床效果有明显差异,12个月时20%(7例)修复体面出现了小的磨耗平面,24个月时达到了45%(16例),并有17%(6例)出现了较大的磨耗平面,修复体的解剖形态均较好。结论:CAD/CAM纳米复合陶瓷嵌体可以作为儿童第一磨牙大面积牙体缺损的有效治疗方案,疗效稳定,满意度高。展开更多
目的:探讨基于风险因素构建的预测模型,预测糖尿病患者感染SARS-CoV-2 (新型冠状病毒)的预后。方法:回顾性分析了239例2022年12月至2023年1月重庆医科大学附属第二医院收治的确诊为SARS-CoV-2感染的糖尿病住院患者。通过电子病历系统收...目的:探讨基于风险因素构建的预测模型,预测糖尿病患者感染SARS-CoV-2 (新型冠状病毒)的预后。方法:回顾性分析了239例2022年12月至2023年1月重庆医科大学附属第二医院收治的确诊为SARS-CoV-2感染的糖尿病住院患者。通过电子病历系统收集患者的相关资料。其中死亡43例,好转出院196例。将患者分为死亡组及存活组,采用单因素Logistic回归分析筛选相关候选因子,再通过多因素Logistic回归分析构建预测模型,使用Bootstrap方法进行内部验证,利用校准曲线、DCA曲线对模型性能进行评估。结果:通过Logistic回归分析,最终有5个因素纳入预测模型,分别为C反应蛋白(CRP) [OR 1.01 (95% CI 1.02)]、白介素6 [OR 1.01 (95% CI 1.01)]、白介素10 [OR 1.04 (95% CI 1.08)]、血氯[OR 0.87 (95% CI 0.99)]、血钠[OR 1.31 (95% CI 1.55)]。经验证该模型在内部验证队列中表现良好。结论:我们通过Logistic回归分析构建的糖尿病患者感染SARS-CoV-2的预后预测模型能够可视化预测结果,并且具有较好的预测效能,对临床制定干预决策有指导意义。Objective: To explore a predictive model based on risk factors to predict the prognosis of diabetic patients infected with SARS-CoV-2. Methods: 239 diabetic inpatients with SARS-CoV-2 infection admitted to the Second Affiliated Hospital of Chongqing Medical University from December 2022 to January 2023 were analyzed retrospectively. The relevant data of the patients was collected by the electronic medical record system. The patients were divided into a death group and a survival group, and the relevant candidate factors were screened using univariate logistic regression analysis, and then the prediction model was constructed by multi-factor logistic regression analysis. The model was internally verified by the Bootstrap method, and the performance of the model was evaluated by the calibration curve and DCA curve. Results: Through logistic regression analysis, five factors were included in the prediction model: C-reactive protein (CRP) [OR 1.01 (95% CI 1.02)], IL-6 [OR 1.01 (95% CI 1.01)], IL-10 [OR 1.04 (95% CI 1.08)], and blood chlorine [OR 0.87 (95% CI 0.99)] and serum sodium [OR 1.31 (95% CI 1.55)]. It is proved that the model performs well in the internal verification queue. Conclusion: The prognosis prediction model of diabetic patients infected with SARS-CoV-2 constructed by Logistic regression analysis can visually predict the results, and has a good predictive efficiency, which is of guiding significance for clinical intervention decision-making.展开更多
文摘目的:观察研究一种纳米复合陶瓷修复儿童第一磨牙大面积牙体缺损的临床效果。方法:针对临床上29例年龄7~15岁,第一磨牙大面积牙体缺损的患者(共35颗),使用CAD/CAM纳米复合陶瓷进行修复,13颗嵌体和22颗高嵌体。修复后24个月内分别对患者进行修复体形状、色泽、舒适性的满意度调查,使用改良的美国公共卫生服务(United States Public Health Service,USPHS)标准分别评价修复后即刻、12个月和24个月时的临床疗效,评价项目包括修复体完整性、继发龋、边缘适合性、牙龈健康状况和磨耗程度。结果:35颗患牙修复后即刻、24个月修复体形状和舒适性主观评价满意度均达100%,24个月时色泽满意度下降为91.4%。修复体完整性、继发龋以及牙龈健康状况24个月内均100%达到A级。边缘适合性12个月时均达到A级,24个月时88.6%(31例)达到A级,有统计学差异。磨耗程度上,12个月、24个月的临床效果有明显差异,12个月时20%(7例)修复体面出现了小的磨耗平面,24个月时达到了45%(16例),并有17%(6例)出现了较大的磨耗平面,修复体的解剖形态均较好。结论:CAD/CAM纳米复合陶瓷嵌体可以作为儿童第一磨牙大面积牙体缺损的有效治疗方案,疗效稳定,满意度高。
文摘目的:探讨基于风险因素构建的预测模型,预测糖尿病患者感染SARS-CoV-2 (新型冠状病毒)的预后。方法:回顾性分析了239例2022年12月至2023年1月重庆医科大学附属第二医院收治的确诊为SARS-CoV-2感染的糖尿病住院患者。通过电子病历系统收集患者的相关资料。其中死亡43例,好转出院196例。将患者分为死亡组及存活组,采用单因素Logistic回归分析筛选相关候选因子,再通过多因素Logistic回归分析构建预测模型,使用Bootstrap方法进行内部验证,利用校准曲线、DCA曲线对模型性能进行评估。结果:通过Logistic回归分析,最终有5个因素纳入预测模型,分别为C反应蛋白(CRP) [OR 1.01 (95% CI 1.02)]、白介素6 [OR 1.01 (95% CI 1.01)]、白介素10 [OR 1.04 (95% CI 1.08)]、血氯[OR 0.87 (95% CI 0.99)]、血钠[OR 1.31 (95% CI 1.55)]。经验证该模型在内部验证队列中表现良好。结论:我们通过Logistic回归分析构建的糖尿病患者感染SARS-CoV-2的预后预测模型能够可视化预测结果,并且具有较好的预测效能,对临床制定干预决策有指导意义。Objective: To explore a predictive model based on risk factors to predict the prognosis of diabetic patients infected with SARS-CoV-2. Methods: 239 diabetic inpatients with SARS-CoV-2 infection admitted to the Second Affiliated Hospital of Chongqing Medical University from December 2022 to January 2023 were analyzed retrospectively. The relevant data of the patients was collected by the electronic medical record system. The patients were divided into a death group and a survival group, and the relevant candidate factors were screened using univariate logistic regression analysis, and then the prediction model was constructed by multi-factor logistic regression analysis. The model was internally verified by the Bootstrap method, and the performance of the model was evaluated by the calibration curve and DCA curve. Results: Through logistic regression analysis, five factors were included in the prediction model: C-reactive protein (CRP) [OR 1.01 (95% CI 1.02)], IL-6 [OR 1.01 (95% CI 1.01)], IL-10 [OR 1.04 (95% CI 1.08)], and blood chlorine [OR 0.87 (95% CI 0.99)] and serum sodium [OR 1.31 (95% CI 1.55)]. It is proved that the model performs well in the internal verification queue. Conclusion: The prognosis prediction model of diabetic patients infected with SARS-CoV-2 constructed by Logistic regression analysis can visually predict the results, and has a good predictive efficiency, which is of guiding significance for clinical intervention decision-making.
