METHODS: This was a cross-sectional study. An online self-administered questionnaire was distributed to Malaysian citizens aged 18-37 years. The questionnaire consisted of 11 questions that investigated their awareness of non-dentists offering orthodontic treatment, the harmful effects of braces fitted by non-dentists, and potential strategies to mitigate this phenomenon.
RESULTS: The study was completed by 426 participants, predominantly Malay, with a mean age of 22.9 years. A total of 76.1% reported awareness of braces fixed by non-dentists, primarily through social media platforms such as Instagram and Facebook. Lower cost emerged as the predominant motive (83.6%) for opting for non-dentist orthodontic treatment, followed by no waiting list (48.8%). Notably, the majority of participants acknowledged the illegality (70%) and potential harm (77%) associated with non-dentists providing orthodontic treatment. Legal enforcement (53.1%) was identified as the preferred method for mitigating this practice. Occupation significantly influenced knowledge of illegal orthodontic treatment (p 0.05).
CONCLUSION: The survey revealed that young adults are aware of and informed about non-dentists offering orthodontic treatment. While they identified cost as the primary reason for seeking such services, they also recognized legislation and public awareness through campaigns and social media as effective strategies to address this issue. Additionally, significant differences in legal awareness were observed among different occupational levels.
PURPOSE: To examine the accuracy of AI-based radiomics in diagnosis, prognosis assessment and predicting the diagnostic value of radiomics for pelvic LN metastasis in cervical cancer patients.
MATERIAL AND METHODS: The study included 118 female patients with 660 LNs and 118 merged LNs. Four imaging histology models-decision tree, random forest, logistic regression, and support vector machine (SVM)-were created in this study. The imaging histology features were extracted from both the independent and merged LN groups. The AUC values for the test sets and the training sets of the four imaging histology models were compared for the independent LN group and the merged LN group. The DeLong test was used to compare the models.
RESULT: The imaging histology prediction model developed in the merged LN group outperformed the independent LN group in terms of test set AUC (0.668 vs. 0.535 for decision tree, 0.841 vs. 0.627 for logistic regression, 0.785 vs. 0.637 for random forest, 0.85 vs. 0.648 for SVM) and accuracy (0.754 vs. 0.676 for decision tree, 0.780 vs. 0.671 for random forest, 0.848 vs. 0.685 for logistic regression, 0.822 vs. 0.657 for SVM).
CONCLUSION: The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.
METHODS: This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019. The study outcome was hospital all-cause mortality. Various machine learning models [logistic regression, random forest (RF), k-nearest neighbor, support vector machine, naïve Bayes, multilayer perception, boosting] were used to predict mortality, and their performance was measured using accuracy, precision, recall, F1 score, and area under the receiver operator characteristic curve (AUC).
RESULTS: Nonsurvivors had significantly greater EuroSCORE II values (1.84 (10.08-3.67) vs. 4.75 (2.54-9.53) %, P <0.001 for survivors and nonsurvivors, respectively). The EuroSCORE II score significantly predicted hospital mortality (OR: 1.13 (95% CI: 1.09-1.18), P <0.001), with an AUC of 0.736. RF achieved the best ML performance (accuracy=98, precision=100, recall=97, and F1 score=98). Explainable artificial intelligence using SHAP demonstrated the most important features as follows: preoperative lactate level, emergency surgery, chronic kidney disease (CKD), NSTEMI, nonsmoking status, and sex. QLattice identified lactate and CKD as the most important factors for predicting hospital mortality this patient group.
CONCLUSION: This study demonstrates the potential of ML, particularly the Random Forest, to accurately predict hospital mortality in patients undergoing CABG for LMCA disease and its superiority over traditional methods. The key risk factors identified, including preoperative lactate levels, emergency surgery, chronic kidney disease, NSTEMI, nonsmoking status, and sex, provide valuable insights for risk stratification and informed decision-making in this high-risk patient population. Additionally, incorporating newly identified risk factors into future risk-scoring systems can further improve mortality prediction accuracy.
AIM: To determine the changes in frequency and pattern of anticholinergic drug use within a low- and middle-income country.
METHOD: Comparisons were made between population-based datasets collected from Malaysian residents aged 55 years and older in 2013-15 and 2020-22. Anticholinergic exposure was determined using the anticholinergic cognitive burden (ACB) tool. Drugs with ACB were categorised according to the Anatomical Therapeutic Chemical (ATC) classification.
RESULTS: A total number of 5707 medications were recorded from the 1616 participants included in the 2013-15 dataset. A total number of 6175 medications were recorded from 2733 participants in 2020-22. Two hundred and ninety-three (18.1%) and 280 (10.2%) participants consumed ≥ 1 medication with ACB ≥ 1 in 2013-15 and 2020-22 respectively. The use of nervous system drugs with ACB had increased (27 (0.47%) versus 39 (0.63%). The use of ACB drugs in the cardiovascular (224 (3.9%) versus 215 (3.4%)) and alimentary tract and metabolism (30 (0.52%) versus 4 (0.06%)) classes had reduced over time. Participants in 2020-22 were significantly less likely than those in 2013-15 to have total ACB = 1 - 2 (odds ratio [95% confidence interval] = 0.473[0.385-0.581]) and ACB ≥ 3 (0.251[0.137 - 0.460]) compared to ACB = 0 after adjustment for potential confounders (p