Objective To study the effect of ladder language training on vocabulary comprehension of children with Down syndrome(DS).Methods Fifteen DS children aged 10～18 years(average 13.89±2.42 years)were in-cluded as the experimental group and 15 DS children aged 10～18 years(average 13.56±1.81 years)were recruited as the control group.The intelligence level of the two groups was evaluated by China Binet-Simon intelligence test.The vocabulary comprehension ability of two groups of children was evaluated by Peabody picture vocabulary test re-vised(PPVT-R).The physiological age,intelligence level and vocabulary comprehension ability before intervention were matched between the two groups.The children in the experimental group had 16 ladder language training twice a week,45 minutes each time by group intervention for 16 times.The children in the control group did not receive this kind of intervention.Through comparison,the effect of ladder language training on DS children's vocabulary comprehension ability was analyzed.Results There was no significant difference in PPVT-R scores between the ex-perimental group(39.6±11.04)and the control group(39.93±10.61)before training(P＞0.05).After training,the PPVT-R score of DS children in experimental group(48.53±11.23)was significantly higher than that in con-trol group(40.2±10.63)(t(28)=2.086,P＜0.05)and was extremely significantly higher than that before training(39.6±11.04)(t(14)=-8.956,P＜0.001).There was no significant difference in PPVT-R scores between DS children in the control group before and after training(t(14)=-1.468,P＞0.05).The scores of children's under-standing abilities of nouns,verbs and adjectives in DS group after training were all higher than 80％,which was sig-nificantly higher than that before training(P＜0.001).Conclusion Ladder language training can significantly im-prove the vocabulary comprehension ability of DS children.

Objective To explore the risk factors of upper limb lymphedema after breast cancer surgery and to construct and validate a risk prediction visualized model.Methods A retrospective analysis was performed on the clinical data of 528 breast cancer patients who underwent surgery.The patients were randomly divided into training set(352 cases)and validation set(176 cases).The train-ing set patients were divided into lymphedema group(67 cases)and non-lymphedema group(285 ca-ses)based on whether they had upper limb lymphedema.Logistic regression analysis was used to screen risk factors for upper limb lymphedema after breast cancer surgery.A risk prediction visualized model was constructed and validated using R software.Results The incidence of upper limb lymphedema after breast cancer surgery was 20.08％(106/528).Multivariate Logistic regression a-nalysis showed that high body mass index(BMI),hypertension,TNM stage Ⅲ a,bilateral lesions,level Ⅲ of axillary lymph node dissection,and postoperative radiotherapy and chemotherapy were inde-pendent risk factors for upper limb lymphedema after breast cancer surgery(P＜0.05).A risk predic-tion nomogram model was constructed based on above six independent risk factors.The calibration curve of the model in the training set and validation set both fitted the ideal curve well;the receiver operating characteristic curve analysis showed that the area under the curve of the nomogram model in the training set and validation set for predicting upper limb lymphedema after surgery was 0.950 and 0.886,respectively;the decision curve analysis showed that the overall net benefitof the model in predicting upper limb lymphedema after surgery in the training set and validation set was higher than that of all patients receiving full intervention or no intervention.Conclusion High BMI,hyperten-sion,stage Ⅲ a of TNM,bilateral lesions,level Ⅲ of axillary lymph node dissection,and postoper-ative radiotherapy and chemotherapy are independent risk factors for upper limb lymphedema after breast cancer surgery.The risk prediction nomogram model constructed based on these factors has good predictive performance.

Objective To explore the risk factors of upper limb lymphedema after breast cancer surgery and to construct and validate a risk prediction visualized model.Methods A retrospective analysis was performed on the clinical data of 528 breast cancer patients who underwent surgery.The patients were randomly divided into training set(352 cases)and validation set(176 cases).The train-ing set patients were divided into lymphedema group(67 cases)and non-lymphedema group(285 ca-ses)based on whether they had upper limb lymphedema.Logistic regression analysis was used to screen risk factors for upper limb lymphedema after breast cancer surgery.A risk prediction visualized model was constructed and validated using R software.Results The incidence of upper limb lymphedema after breast cancer surgery was 20.08％(106/528).Multivariate Logistic regression a-nalysis showed that high body mass index(BMI),hypertension,TNM stage Ⅲ a,bilateral lesions,level Ⅲ of axillary lymph node dissection,and postoperative radiotherapy and chemotherapy were inde-pendent risk factors for upper limb lymphedema after breast cancer surgery(P＜0.05).A risk predic-tion nomogram model was constructed based on above six independent risk factors.The calibration curve of the model in the training set and validation set both fitted the ideal curve well;the receiver operating characteristic curve analysis showed that the area under the curve of the nomogram model in the training set and validation set for predicting upper limb lymphedema after surgery was 0.950 and 0.886,respectively;the decision curve analysis showed that the overall net benefitof the model in predicting upper limb lymphedema after surgery in the training set and validation set was higher than that of all patients receiving full intervention or no intervention.Conclusion High BMI,hyperten-sion,stage Ⅲ a of TNM,bilateral lesions,level Ⅲ of axillary lymph node dissection,and postoper-ative radiotherapy and chemotherapy are independent risk factors for upper limb lymphedema after breast cancer surgery.The risk prediction nomogram model constructed based on these factors has good predictive performance.