The prevalence of enamel and dentine caries lesions and their determinant factors among children living in fluoridated and non-fluoridated areas.

OBJECTIVE: To determine the prevalence and severity of dental caries (at dentine and enamel levels of diagnosis) amongst Malaysian children and to investigate determinant factors associated with caries detection at these different thresholds. METHODS: This study involved life-long residents aged 12 years-old in fluoridated and non-fluoridated areas in Malaysia (n=595). The survey was carried out in 16 public schools by a calibrated examiner, using ICDAS-II criteria. A questionnaire on socio-demographic and oral hygiene practices was self-administered by parents/guardians. Data were analysed using Mann-Whitney U tests and logistic regression. RESULTS: The overall response rate was 74.4%. Caries prevalence at the dentine level or at the dentine and enamel level was significantly (p⟨0.001) higher among children in the non-fluoridated area (D1₋6MFT⟩0 = 82.4%, D4₋6MFT⟩0 = 53.5%) than in the fluoridated area (D1₋6MFT⟩0 = 68.7%, D4₋6MFT⟩0 = 25.5%). Considering only the decayed component of the index, no significant differences were observed between the two areas when the detection threshold was set at enamel caries (D1₋3) (p=0.506). However, when the detection criteria were elevated to the level of caries into dentine (D4₋6) there were clear differences between the fluoridated and non-fluoridated areas (p=0.006). Exposure to fluoridated water proved a significant predictor for lower caries prevalence in the statistical model. Children whose father and mother had a low monthly income had a significantly higher dentine caries prevalence. CONCLUSION: Results confirmed existing evidence of the benefit of water fluoridation in caries prevention. Detection criteria set at caries into dentine shows clear differences between fluoridated and non-fluoridated areas. Exposure to fluoridated water and socio-economic status were associated with caries prevalence.

Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data.

Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorithms such as deep learning can be tested with the aim of improving the models and prediction accuracy. In this study, we used machine learning and deep learning approaches to predict breast cancer survival in 4,902 patient records from the University of Malaya Medical Centre Breast Cancer Registry. The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respectively, with 88.2 %, 83.3 % and 82.5 % accuracy in the tested samples. Support vector machine (SVM) came out to be lower with 80.5 %. In this study, tumour size turned out to be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.

Senior dentists' perceptions of dental therapists' roles and education needs in Malaysia.

UNLABELLED: To describe the perceptions of senior dental officers (SDOs) on the roles of dental therapists (DTs) and their education needs in Malaysia. METHODS: A cross-sectional survey was conducted using a self-administered postal questionnaire targeting all 112 SDOs in the Malaysian Ministry of Health. The SDOs were asked about their perceptions of DT's roles in relation to clinical tasks, oral health promotion, administration and the dental team and their perceptions of DT's future education needs. Data were analysed using spss software, version 17 (SPSS Inc., Chicago, IL, USA). RESULTS: The response rate was 60%. A majority of SDOs were women (68%) with a mean age of 44.9 (SD: 8.04). Generally, the majority of SDOs perceived the current roles of DT in non-complex clinical tasks such as examination and diagnosis, preventive treatment, extraction of deciduous teeth and oral health promotion as very important. Fewer than half of SDOs perceived DT's role in the extraction of permanent teeth as important. Most SDOs perceived the need to train DT in 'scaling and polishing for adults' (80.5%), 'delivering inferior alveolar nerve block' (57.3%) and 'pulp therapy' (59.2%). They also had positive perceptions of providing education for DT up to degree level (70.8%). CONCLUSION: This study suggests that Malaysian SDOs have positive perceptions of the current roles of DT and of the expansion of some of their clinical tasks to include broader client groups through further training and education. These findings indicate a need to revise the current curriculum and legislation pertaining to DT's education and scope of practice in Malaysia.