College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in coll...College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in college students＇ English study. Therefore, to gradually improve all the students＇ English, graded teaching becomes the best choice. In this thesis, graded teaching will be studied from several respects. And I hope it can give some slight help to some of college teachers.展开更多
目的探讨能谱CT联合超声C-TIRADS分级鉴别甲状腺结节良恶性的价值。方法选取2023年5月至2024年5月滨州医学院附属医院收治的70例甲状腺结节患者进行回顾性分析,其中良性结节26例,恶性结节44例。术前均行超声检查及能谱CT增强扫描。比较...目的探讨能谱CT联合超声C-TIRADS分级鉴别甲状腺结节良恶性的价值。方法选取2023年5月至2024年5月滨州医学院附属医院收治的70例甲状腺结节患者进行回顾性分析,其中良性结节26例,恶性结节44例。术前均行超声检查及能谱CT增强扫描。比较两组年龄、性别、结节长径、能谱CT参数等资料。通过单因素及多因素分析筛选出能谱CT的独立预测因素,引入超声C-TIRADS分级构建列线图模型。采用Bootstrap法迭代1000次,内部验证模型的稳定性。采用独立样本t检验、Mann-Whitney U检验、χ^(2)检验进行统计分析。结果良性组和恶性组能谱参数[包括动脉期及静脉期碘浓度(iodine concentration,IC)、标准化碘浓度(normal iodine concentration,NIC)、能谱曲线斜率(slope of the energy spectrum curve,λHU)]以及结节长径比较,差异均有统计学意义(均P<0.05)。多因素分析表明,动脉期IC及静脉期NIC是鉴别甲状腺结节良恶性的独立预测因素(均P<0.05)。基于上述变量构建预测模型,该模型曲线下面积(AUC)为0.940。利用超声C-TIRADS分级诊断甲状腺结节良恶性,其AUC为0.823。超声C-TIRADS分级联合能谱CT参数构建列线图,其AUC为0.982。校准曲线显示,列线图校准度表现优秀,Brier评分为0.051。决定曲线分析显示,在广泛阈值概率范围内,列线图均表现出较好的临床净收益。应用Bootstrap法进行1000次迭代,计算平均AUC来对列线图模型进行内部验证,平均AUC为0.961。结论能谱CT预测模型AUC高于超声C-TIRADS分级。联合模型可以提高能谱CT及超声C-TIRADS分级鉴别甲状腺结节良恶性的效能。展开更多
文摘College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in college students＇ English study. Therefore, to gradually improve all the students＇ English, graded teaching becomes the best choice. In this thesis, graded teaching will be studied from several respects. And I hope it can give some slight help to some of college teachers.
文摘目的探讨能谱CT联合超声C-TIRADS分级鉴别甲状腺结节良恶性的价值。方法选取2023年5月至2024年5月滨州医学院附属医院收治的70例甲状腺结节患者进行回顾性分析,其中良性结节26例,恶性结节44例。术前均行超声检查及能谱CT增强扫描。比较两组年龄、性别、结节长径、能谱CT参数等资料。通过单因素及多因素分析筛选出能谱CT的独立预测因素,引入超声C-TIRADS分级构建列线图模型。采用Bootstrap法迭代1000次,内部验证模型的稳定性。采用独立样本t检验、Mann-Whitney U检验、χ^(2)检验进行统计分析。结果良性组和恶性组能谱参数[包括动脉期及静脉期碘浓度(iodine concentration,IC)、标准化碘浓度(normal iodine concentration,NIC)、能谱曲线斜率(slope of the energy spectrum curve,λHU)]以及结节长径比较,差异均有统计学意义(均P<0.05)。多因素分析表明,动脉期IC及静脉期NIC是鉴别甲状腺结节良恶性的独立预测因素(均P<0.05)。基于上述变量构建预测模型,该模型曲线下面积(AUC)为0.940。利用超声C-TIRADS分级诊断甲状腺结节良恶性,其AUC为0.823。超声C-TIRADS分级联合能谱CT参数构建列线图,其AUC为0.982。校准曲线显示,列线图校准度表现优秀,Brier评分为0.051。决定曲线分析显示,在广泛阈值概率范围内,列线图均表现出较好的临床净收益。应用Bootstrap法进行1000次迭代,计算平均AUC来对列线图模型进行内部验证,平均AUC为0.961。结论能谱CT预测模型AUC高于超声C-TIRADS分级。联合模型可以提高能谱CT及超声C-TIRADS分级鉴别甲状腺结节良恶性的效能。
