The overarching objective of this study was to assess learning satisfaction among students and to determine whether online-learning self-efficacy was associated with online learning satisfaction during the emergency transition to remote learning. This cross-sectional study involved a survey distributed to 22 Saudi Arabian universities. The survey used in this study consisted of an online learning self-efficacy (OLSE) questionnaire and an electronic learning (e-learning) satisfaction questionnaire. A total of 1,226 respondents voluntarily participated in and completed the survey. Students in medical fields made up 289 (23.6%). A Kruskal–Wallis H test and a chi-square test were used to compare the student’s satisfaction based on the educational variables. Spearman’s correlation and multiple linear regression analyses were performed to assess the association between self-efficacy and satisfaction. The findings revealed degrees of satisfaction ranging between high satisfaction and dissatisfaction. The majority of students (51%) expressed high satisfaction, and 599 students (49%) reported experiencing a low level of satisfaction with e-learning. A comparison of groups with low and high satisfaction scores revealed a significant difference in the OLSE. High satisfaction was positively correlated with the OLSE domains: time management, technology, and learning. The OLSE regression analysis model significantly predicted satisfaction. It showed that the model, corrected for education level and grade point average of the students, significantly predicted e-learning satisfaction (F = 8.04, R² = 0.59, p = .004). The study concluded that students’ satisfaction with the e-learning experience is influenced by e-learning self-efficacy. The study’s findings lead to the practical implications and identify the need to improve the remote learning, time management and technology self-efficacy to enhance students’ satisfaction.

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

Background: Breast cancer (BC) is one of the leading causes of mortality worldwide. There are observed disparities in patients with disability as compared to those without disability, which leads to poor BC screening attendance, thereby worsening disease management. Aim: The aim of this systematic review is to investigate if there are disparities in screening rates in women with disability as compared to those without disability, as well as the different factors that pose barriers to patients with disability for enrolment in BC screening programs. Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed published articles between 2008 and 2023, which assessed different factors that contributed to poor attendance in BC screening programs held across different countries. Detailed study characteristics were obtained, and methodological quality assessment was performed on the individual studies included in this review. Result: A total of fifty-three articles were identified as eligible studies based on the pre-defined inclusion and exclusion criteria. These included 7,252,913 patients diagnosed with BC (913,902 patients with disability/6,339,011 patients without disability). The results revealed there are demographic, clinical, financial, and service-related barriers that contributed to lower screening rates in disabled patients as compared to non-disabled. Patient age is the most common factor, with the highest effect observed for 80 years (vs. 30–44 years) [odds ratio (OR) = 13.93 (95% confidence interval (CI) = 8.27–23.47), p < 0.0001], followed by race/ethnicity for Hispanic (vs. non-Hispanic white) [OR = 9.5 (95%CI = 1.0–91.9), p < 0.05]. Additionally, patients with multiple disabilities had the highest rate of dropouts [OR = 27.4 (95%CI = 21.5–33.3)]. Other factors like education, income, marital status, and insurance coverage were essential barriers in screening programs. Conclusions: This study presents a holistic view of all barriers to poor BC screening attendance in disabled patients, thereby exacerbating health inequalities. A standardized approach to overcome the identified barriers and the need for a tailored guideline, especially for disability groups, is inevitable.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDI_vol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Introduction: Breast cancer screening remains pivotal in early detection and intervention. However, disparities persist, particularly among women with disabilities, necessitating a comprehensive understanding of their screening practices. This study aims to investigate breast cancer screening behaviours in Saudi women with disabilities.Methods: A cross-sectional study conducted in Saudi Arabia surveyed 307 women with disabilities, evaluating their screening frequency, knowledge of mammography, disability types, and duration. The Statistical Package for the Social Sciences (SPSS) was employed for data analysis.Results: The study found that 70.4% of participants had irregular breast cancer screenings, and 92.5% lacked tailored information on breast cancer screening. The primary sources of information were support groups (59.3%) and healthcare professionals (25.4%). Significant associations were observed between education levels and awareness of the importance of mammography and the increased risk of breast cancer in individuals with disabilities. Notably, participants with higher education levels demonstrated greater awareness.Conclusion: The findings highlight substantial gaps in breast cancer screening practices and knowledge among Saudi women with disabilities. There is a critical need for tailored educational programs, accessible information, and targeted awareness campaigns to address these disparities. Enhancing the accessibility of screening services and information for this demographic is essential for improving healthcare equity and outcomes.