Gauging the Awareness of Physicians in Saudi Arabia Regarding Risk Factors for Thyroid Cancer.

Background The prevalence of obesity and thyroid cancer (TC) is increasing worldwide, and obesity is a risk factor for TC. Objectives This study aimed to elucidate physicians' awareness of obesity as a risk factor for TC. Materials and methods A cross-sectional, self-report online questionnaire was distributed to physicians in all regions of Saudi Arabia. The questionnaire comprised sociodemographic data and questions concerning the risk factors for TC, including obesity. Results A total of 310 physicians participated in this study. Of the respondents, 35.8% (n = 111) were aged 30-40 years, 40.6% (n = 126) were board certified, and 52.3% (n = 162) had >10 years of experience. Only 36.8% (n = 114) of respondents were familiar with the relationship between obesity and TC risk (P < 0.001). In terms of knowledge of obesity as a risk factor for TC, a significant difference was observed for the following sociodemographic characteristics: sex, educational attainment, and years of experience. A significant difference was also observed with awareness of other risk factors for TC. Conclusions In light of the limited awareness of the correlation between obesity and TC, the most effective approach to address these misconceptions would be to implement diverse and ongoing medical education initiatives.

Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low-middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case-control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.

Depression and anxiety among parents and caregivers of autistic spectral disorder children.

OBJECTIVE: To evaluate the presence of depression and anxiety in parents/caregivers of autistic spectral disorder (ASD) children, and to identify associated factors. METHODS: In this retrospective cohort study carried out between July and December 2011, parents/caregivers of at least one child diagnosed with an ASD (cases group), were recruited through the Saudi Charitable Society of Autism Families and the Autism Clinic in the Pediatric out-patient clinic in King Fahad National Guard Hospital, King Abdulaziz Medical City (KAMC), Riyadh, Kingdom of Saudi Arabia. The parents/caregivers of a normally developed child (control group) were recruited from the Well Child Clinic at King Abdulaziz Medical City, Riyadh. We used a self-reported questionnaire containing questions on demographic data, as well as the Arabic version of the hospital anxiety and depression scale. RESULTS: The study included 100 parents/caregivers, 50 cases and 50 controls. More than 50% of the control group was in the age group 26-30 years (56%), while 42% of cases were in the age group 31-35 years. Time lapsed since autism diagnosis was over 3 years in one-third of cases. Twenty-two percent of cases, and only 2% of control parents/caregivers had a history of psychiatric problems (p=0.002). Both the mean depression score, and the mean anxiety score was significantly higher among cases when compared with controls, p<0.001. CONCLUSION: Autism is associated with burden and stress for parents/caregivers of the affected child. The demands placed by the disability contribute to a higher overall incidence of depression and anxiety among parents/caregivers.