To assess the prevalence of burnout symptoms among preclinical and clinical medical students studying at AlFaisal University in Riyadh, Saudi Arabia. A cross-sectional study was conducted using Maslach Burnout Inventory questionnaire on 276 medical students from Alfaisal University, Riyadh, Saudi Arabia. The study was approved by Alfaisal University research ethics committee. Chi-square test was used to identify statistically significant differences, and binary logistic regression was used to identify predictors of burnout. 276 entered into final data analysis with a mean age 20.62 ± 1.58, of whom 54% were males, and 46% were females. The overall burnout prevalence was 13.4%, of which PA was the most prevalent domain of burnout with 64.9%. Female gender was a significant predictor of EE and DP [OR = 4.34; 95% Cl 1.86–10.13; P-value 0.001] and [OR = 2.01; 95% Cl 1.07–3.79; P-value 0.030] respectively as per multivariate analysis for demographic characteristics. Regarding the total level of burnout, females (75.7%) had significantly higher levels of burnout compared to males (41.4%); (P-value < 0.001). Burnout is prevalent among medical student. Gender was found to exhibits effect on the burnout. Mutual proactive strategies and reactive coping mechanisms between the students and the universities are encouraged to prevent and reduce burnout among medical students.

The medical student population is believed to be at an increased risk for sleep deprivation. Little is known about students’ perceptions towards sleep deprivation and its relationship to academic performance. The aim of study is to explore the perceptions of medical students and their academic advisors about sleep deprivation and its relationship to academic performance. The study took place at Alfaisal University, College of Medicine, Riyadh, Saudi Arabia. An online, anonymous, cross-sectional, self-rating survey was administered to first-, third-year students and their academic advisors. Two-tailed Mann–Whitney U test was used to compare the mean 5-point Likert scale responses between students according to gender, academic year and cumulative grade point average (cGPA). A total of 259 students and 21 academic advisors participated in the survey (response rates: 70.6 and 84 %, respectively). The vast majority of students agreed that sleep deprivation negatively affects academic performance (78.8 %) and mood (78.4 %). Around 62.2 and 73.7 % of students agreed that the demanding medical curriculum and stress of final exams lead to sleep deprivation, respectively. While 36.7 % of students voiced the need for incorporation of curricular separate courses about healthy sleep patterns into medical curriculum, a much greater proportion of students (45.9 %) expressed interest in extracurricular activities about healthy sleep patterns. Interestingly, only 13.5 % of students affirmed that they were counselled about sleep patterns and academic performance by their academic advisors. There were several statistically significant differences of means of students’ perceptions according to gender, academic year and cGPA. Despite almost all academic advisors (95.5 %) asserted the importance of sleep patterns to academic performance, none (0 %) inquired about sleep patterns when counselling students. Nineteen academic advisors (90.5 %) recommended incorporation of sleep patterns related learning into medical curricula; among those, only 1 (n = 1/19; 5.3 %) recommended learning as a separate course whereas the majority (n = 18/19; 94.7 %) recommended learning in forms of extracurricular activities and integration into relevant ongoing courses. Our results showed that students had correct conceptions about the negative impact of sleep deprivation on academic performance and mood. Also, our results highlighted the need for curricular/extracurricular education and counseling about healthy sleep patterns.

This study explores the implementation of Team-Based Learning (TBL) at Alfaisal University’s College of Medicine through the lens of Complex Adaptive Systems (CAS) theory. The research question investigates how the application of CAS principles can enhance the implementation and effectiveness of TBL in medical education. The study employed a convergent parallel mixed methods longitudinal design, integrating quantitative performance metrics and qualitative themes. Quantitative analysis revealed modest improvements in individual and team-based learning scores, with a promising trend of students moving from the lower to the higher quartiles over time. Qualitative insights aligned with CAS principles, highlighting the adaptive implementation, emergent outcomes, self-organization, positive feedback loops, and depth of learning facilitated by TBL. The findings demonstrate the value of a CAS-informed approach in navigating the complexities of educational change and fostering a more resilient and adaptive educational model. The study contributes to the understanding of how CAS theory can guide the successful implementation of innovative pedagogies like TBL in medical education.

Medical education faces increasing demand for scalable grading solutions. Large language models have been proposed as automated graders for high-stakes assessments, but evidence for their reliability in multimodal practical examinations remains limited. This retrospective inter-rater reliability study compared three LLMs: ChatGPT-4o, Gemini 2.5 Flash, and Claude 3.5 Haiku with original human reference scores on 19 integrated anatomy, histology, and physiology objective structured practical examination items completed by 309 pre-medical students. Human reference scores were assigned during live summative grading by two content experts who divided items, with each item marked by one expert across all students; no human inter-rater reliability estimate was available. Items required simultaneous image interpretation and short-answer responses. Models were evaluated using zero-shot prompting reflecting deployment-realistic conditions. Agreement was assessed using Spearman's ρ, Cohen's κ, intraclass correlation coefficients, and Bland-Altman analysis. Item-level gap analysis compared student success rates with LLM performance across items. Rank-order correlations were strong across all models (ρ = 0.784-0.921), but categorical agreement diverged substantially. Pass/fail agreement ranged from 52.1% (ChatGPT; κ = 0.067, slight) to 84.1% (Claude; κ = 0.680, substantial). Bland-Altman analysis showed inconsistent systematic bias: Claude over-scored by +2.5 points, Gemini under-scored by -5.0 points, and ChatGPT by -10.0 points. Item-level gap analysis highlighted three recurring divergence patterns: non-standard histological staining, three-dimensional spatial reasoning from two-dimensional images, and semantic inflexibility in short-answer evaluation. The most extreme case was a pelvic three-dimensional model item on which 93.3% of students succeeded by human grading but all three LLMs assigned a mean score of zero. Strong rank-order correlation alone does not support LLM grading for categorical decisions in high-stakes assessment contexts. LLMs may serve as assessment assistants for first-pass ranking and discrepancy flagging, but item-level human review of flagged discordances, error-profile monitoring, and preserved human authority over pass/fail decisions are required before high-stakes deployment.

Epithelioid sarcoma represents a rare and aggressive soft tissue malignancy characterized by SMARCB1/INI1 inactivation and propensity for local recurrence and distant metastasis. Comprehensive population-based analyses of prognostic determinants remain limited. We evaluated survival outcomes and identified independent prognostic factors using the Surveillance, Epidemiology, and End Results (SEER) database. We retrospectively analyzed 1123 patients diagnosed with epithelioid sarcoma between 2001 and 2022 from the SEER 17 registries database. Cox proportional hazards regression models were employed to identify prognostic factors for cancer-specific survival. Kaplan–Meier analysis with log-rank testing was performed for survival comparisons. The cohort comprised 634 males (56.5%) and 489 females (43.5%) with a mean age of 47.1 ± 21.3 years. Stage distribution revealed localized disease in 45.3%, regional involvement in 26.5%, and distant metastases in 28.2% of evaluable cases. Surgical resection was performed in 70.8% of patients. The median overall survival was 84 months, with a 5-year cancer-specific survival of 53.7%. Multivariate analysis identified distant stage (adjusted hazard ratio [aHR] = 5.379, 95% confidence interval [CI]: 3.652–7.921, P < .001), regional stage (aHR = 2.474, 95% CI: 1.711–3.578, P < .001), tumor size per centimeter (aHR = 1.078, 95% CI: 1.052–1.104, P < .001), and age per year (aHR = 1.011, 95% CI: 1.004–1.018, P = .002) as adverse prognostic factors. Surgical intervention demonstrated substantial survival benefit (aHR = 0.397, 95% CI: 0.280–0.563, P < .001). This contemporary population-based analysis represents the largest evaluation of epithelioid sarcoma outcomes to date, encompassing more than twice the sample size of prior SEER analyses. Stage at diagnosis emerges as the predominant prognostic determinant, with surgical resection conferring consistent survival benefit across disease stages. Compared with historical cohorts, observed improvements in 5-year survival likely reflect advances in diagnostic recognition and multimodal management.

Dedifferentiated liposarcoma (DDLPS) represents a high-grade sarcoma characterized by transition from well-differentiated liposarcoma to non-lipogenic sarcoma. Population-based data examining prognostic factors and treatment outcomes remain limited. This study aimed to identify predictors of survival and evaluate treatment modalities in patients with DDLPS using the Surveillance, Epidemiology, and End Results database. We identified 3962 patients diagnosed with DDLPS between 2000 to 2022 from the Surveillance, Epidemiology, and End Results database. Patient demographics, tumor characteristics, treatment modalities, and survival outcomes were analyzed. Cox proportional hazards regression models were utilized to identify independent prognostic factors for overall survival (OS) and cancer-specific survival. The cohort had a median age of 66 years (interquartile range 57–75), with male predominance (67.5%). Primary tumors were predominantly located in soft tissues (51.4%) and retroperitoneum (36.1%). The median tumor size was 145mm (interquartile range 82–220 mm). Surgery was performed in 85.4% of patients, while radiation therapy and chemotherapy were administered to 33.5% and 18.2%, respectively. Median OS was 54.0 months with 1-year, 2-year, 3-year, and 5-year survival rates of 78.8% (95% confidence intervals [CIs]: 77.5–80.1%), 67.9% (95% CI: 66.4–69.4%), 59.6% (95% CI: 58.0–61.3%), and 47.6% (95% CI: 45.8–49.4%), respectively. In multivariate analysis including 1507 patients with complete staging data, independent predictors of worse OS included advanced age (hazard ratios [HR] = 1.42 per 10 years, 95% CI: 1.34–1.50, P < .0001), regional stage (HR = 1.30, 95% CI: 1.13–1.48, P < .05), distant stage (HR = 2.45, 95% CI: 2.02–2.97, P < .0001), and increasing tumor size (HR = 1.01 per cm, 95% CI: 1.01–1.01, P < .0001). Surgical resection conferred significant survival benefit (HR = 0.29, 95% CI: 0.23–0.36, P < .0001), as did radiation therapy (HR = 0.75, 95% CI: 0.66–0.86, P < .0001). Paradoxically, chemotherapy was associated with worse survival (HR = 1.42, 95% CI: 1.20–1.69, P < .0001). Retroperitoneal location demonstrated worse prognosis compared to non-retroperitoneal sites (median OS: 46.0 vs 60.0 months, log-rank P = .0001; median cancer-specific survival: 84.0 vs 122.0 months, log-rank P < .0001). This large population-based analysis demonstrates that surgical resection remains the cornerstone of treatment for DDLPS, providing substantial survival benefit. Advanced stage, older age, and larger tumor size independently predict poor outcomes. The adverse association with chemotherapy likely reflects selection bias, with systemic therapy reserved for aggressive or advanced disease.