Background
This study aimed to evaluate the associations among oral health-related knowledge, attitudes, practice (KAP), self-rated oral health and oral health-related quality of life (OHRQoL) among Chinese college students.
Methods
Of the 2000 participants, 1751 (87.55%) students answered an online questionnaire between October 2019 and January 2020. The questionnaire included demographic characteristics, knowledge, attitudes, and practice related to oral health, self-rated oral health, and OHRQoL. Structural equation modelling was applied to assess the associations among study variables.
Results
Among the total students, oral health-related knowledge and attitudes were satisfactory, while the oral health practice was not optimistic. The final model showed satisfactory fitness to the data. Oral health knowledge was associated with attitudes directly and positively. Attitudes toward oral health had a direct and positive effect on practice. Oral health knowledge had an indirect effect on practice through attitudes. Oral health practice was directly associated with self-rated oral health. Oral health knowledge, practice, and self-rated oral health all affected OHRQoL directly and positively, while attitudes had a direct negative impact on OHRQoL.
Conclusions
OHRQoL was influenced by oral health knowledge, attitudes, practice, and self-rated oral health. Our findings support the KAP theory. Limitations of the KAP model were also found.
Aim
Breast cancer is the most frequent malignant tumor among Chinese women. Breast self‐examination (BSE) is a simple, effective method for early detection of screening and it is essential for the prevention and control of breast cancer. The aim of this study was to create a hypothetical model to determine the factors influencing women's BSE behavior in Eastern China.
Methods
A survey was conducted using an online questionnaire and targeting 1200 women aged 18–70 years in Eastern China. Collected data were analyzed using ibm spss 25.0 and amos 24.0 software.
Results
The final model showed a desirable fitness to sample data. A direct positive relationship exists between knowledge on risk factors and BSE. A direct positive association was found between positive attitudes and BSE, while negative attitudes have a direct negative impact on BSE. Objective factors not only had a significant direct impact on BSE, but also directly affected the positive attitudes. Positive attitudes play an intermediary role between objective factors and BSE.
Conclusion
Knowledge on risk factors about breast cancer, attitudes toward BSE and objective factors are new predictors which may influence BSE by using the structural equation modeling method.
Aims: This study aimed to create a structural equation model to evaluate the associations among demographic factors, health education, breast cancer-related knowledge, attitudes, and breast self-examination behavior among Chinese female college students. Methods: A survey was undertaken using a self-administered questionnaire and targeting 2233 students from Eastern China. Structural equation modeling with the bootstrap approach was utilized to estimate the interrelationships and the strength of the associations among measured variables based on the hypothetical model. Results: Among the total participants, 14.7% of the female college students reported having performed breast self-examination. The final structural equation model showed highly satisfactory fitness on indices. Breast self-examination behavior was significantly linked to demographic factors, breast cancer relatedknowledge, attitudes, and health education. Health education had the greatest influence on breast selfexamination behavior. In addition, breast cancer related-knowledge was significantly associated with demographic factors and health education. Health education and knowledge all significantly affected attitudes towards breast cancer. Conclusion: Breast self-examination behavior was influenced by demographic factors, breast cancer relatedknowledge, attitudes towards breast cancer, and health education in a sample of female college students in China. Health education was the most important predictor of breast self-examination behavior.
PurposeThe aim of this study was to predict standard uptake values (SUVs) from computed tomography (CT) images of patients with lung metastases from differentiated thyroid cancer (DTC-LM).MethodsWe proposed a novel SUVs prediction model using 18-layer Residual Network for generating SUVmax, SUVmean, SUVmin of metastatic pulmonary nodes from CT images of patients with DTC-LM. Nuclear medicine specialists outlined the metastatic pulmonary as primary set. The best model parameters were obtained after five-fold cross-validation on the training and validation set, further evaluated in independent test set. Mean absolute error (MAE), mean squared error (MSE), and mean relative error (MRE) were used to assess the performance of regression task. Specificity, sensitivity, F1 score, positive predictive value, negative predictive value and accuracy were used for classification task. The correlation between predicted and actual SUVs was analyzed.ResultsA total of 3407 nodes from 74 patients with DTC-LM were collected in this study. On the independent test set, the average MAE, MSE and MRE was 0.3843, 1.0133, 0.3491 respectively, and the accuracy was 88.26%. Our proposed model achieved high metric scores (MAE=0.3843, MSE=1.0113, MRE=34.91%) compared with other backbones. The predicted SUVmax (R2 = 0.8987), SUVmean (R2 = 0.8346), SUVmin (R2 = 0.7373) were all significantly correlated with actual SUVs.ConclusionThe novel approach proposed in this study provides new ideas for the application of predicting SUVs for metastatic pulmonary nodes in DTC patients.
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