A fad diet is a trendy dietary pattern that promises quick and easy weight loss. It has become popular among people due to increasing obesity and social media. The aim of the study was to analyze the use of fad diets among office workers who spend most of the day sitting at a desk. The research design was cross-sectional in 152 office workers aged over 18 years. The survey consisted of 24 questions, and four sections. Pearson chi-square, Fisher's exact test and logistic regression analysis were used for data analysis. Of the 50 (32.89%) participants who tried a fad diet, the majority were female (86%). The ketogenic diet was more likely to follow a fad diet by participants (40%). The influence of person to person (50%) was commonly reported as a motivating factor for using fad diets by participants who tried them. In addition, the majority of participants (68%) who adhered fad diets reported that they achieved the desired result. Fad diet users and non-users differed by gender (p = .003), satisfaction with current weight (p <.000) and BMI (p= .021). The use of fad diets was positively correlated with some variables, including gender (p = .020), and satisfaction with current weight, body image (p = .006). In addition, when asked by 100 participants why they had not consulted qualified professionals, more than half (59%) replied: "I never thought about it.". Some participants described, "Because I did not know who and where to ask for advice" (27%), and "Because it's expensive" (7%). In summary, the ketogenic diet was the most common fad diet among office workers in Ulaanbaatar. Interpersonal influence was a motivating factor for participants to start the diet. A large percentage of respondents said they had never thought to ask a qualified person about fad diets.

Background: Oral diseases remain a major public health challenge globally and in Hungary, where untreated caries, periodontal disease, and edentulism are highly prevalent. Diet is a key modifiable determinant of oral health, yet the cumulative impact of multiple unfavorable nutritional behaviors on self-perceived oral health has not been assessed at the national level in Hungary, nor have population-attributable fraction (PAF) estimates been reported. Methods: This cross-sectional study analyzed data from 5146 adults in the 2019 Hungarian European Health Interview Survey. A six-component nutritional-risk score was constructed from self-reported dietary behaviors (fruit intake, vegetable intake, sugary soft drink consumption, sweets consumption, processed meat consumption, and water intake). Survey-weighted logistic regression estimated associations between nutritional-risk categories and poor self-perceived oral health, adjusting for sociodemographic, lifestyle, and health-related covariates. Counterfactual PAFs with 95% Monte Carlo uncertainty intervals (1000 bootstrap replications) were estimated for three nutritional-risk scenarios. Results: The weighted prevalence of poor self-perceived oral health was 19.2%. Compared with participants with 0–1 unfavorable nutritional factors, those with 3 factors (OR = 1.37; 95% CI: 1.09–1.73) and 4–6 factors (OR = 1.46; 95% CI: 1.15–1.85) had significantly higher odds of poor oral health. Low water intake was independently associated with poor oral health (OR = 1.38; 95% CI: 1.16–1.64). The main counterfactual PAF analysis estimated that 16.1% (Monte Carlo 95% UI: 5.93–26.48%) of poor oral health burden was attributable to unfavorable nutritional profiles. High nutritional risk was also associated with active caries, gum bleeding, and tooth loss. Conclusions: Cumulative nutritional risk is independently associated with poor self-perceived oral health in Hungarian adults. Integrating dietary improvements into oral health prevention strategies may substantially reduce the population-level burden of oral disease.

Introduction: Head and neck cancers are frequently diagnosed at advanced stages, resulting in poor survival despite therapeutic advances, highlighting the need for earlier identification within routine clinical care. Routinely collected electronic health record data provide a potential source of longitudinal clinical signals, although the clinical applicability of machine learning models remains limited by concerns regarding interpretability and real-world integration. Methods: This retrospective cohort study included 157,031 patients treated at the University of Debrecen Clinical Centre between 2007 and 2022, with head and neck cancer defined using ICD-10 codes C01 to C14. A total of 1397 variables, including demographic characteristics, ICD based comorbidities, and laboratory parameters, were reduced to 91 features using variance filtering and elastic net penalized Cox regression. Three survival modelling approaches were developed and compared, including CoxNet, Random Survival Forest, and XGBoost with a Cox objective. Results: The XGBoost model demonstrated the highest predictive performance with a mean concordance index of 0.916, followed by Random Survival Forest at 0.892 and CoxNet at 0.886, with acceptable calibration across models. Risk stratification showed clear separation between low, medium, and high-risk groups. Model interpretability using SHapley Additive exPlanations indicated that predictions were driven by a combination of demographic factors, laboratory markers, and clinically relevant diagnosis codes, reflecting both distal risk gradients and proximal clinical signals. Conclusions: These findings suggest that explainable machine learning applied to routine clinical data can support accurate and clinically interpretable risk stratification, with potential utility for opportunistic early identification of high-risk patients within existing healthcare pathways. Clinical Relevance: Explainable EHR-based survival models may support opportunistic identification of patients at increased head and neck cancer risk within routine clinical workflows, potentially improving triage and referral.

Background: With the rapid emergence of novel survival models and feature selection methods, comparing them with traditional approaches is essential to define contexts of optimal performance. Methods: This study systematically evaluates nine survival models combined with nine feature selection methods for predicting the occurrence of angina pectoris using electronic health record (EHR) data from a Hungarian hospital (n = 29,655, features = 1150). Performance was assessed with the concordance index (C-index) and integrated Brier score (IBS) to compare predictive accuracy across methods. Results: Tree-based survival models, particularly gradient-boosted survival (GBS) and random survival forest (RSF), consistently outperformed conventional approaches in terms of C-index, but showed slightly worse calibration as reflected in their higher IBSs. The best-performing model was RSF, which was optimized using Bayesian hyperparameter tuning. For feature selection, tree-based methods such as Boruta and RSF-based approaches showed superior performance. We further identified clusters of feature selection methods and generated consensus feature sets. We also analyzed the internal relationships between the selected features. Survival model performance was also examined over time using the time-dependent Area Under the Curve (AUC) based on the best-performing feature set. Conclusions: Our findings highlight the substantial impact of recent methodological innovations in survival analysis, which offer significant gains in predictive accuracy and efficiency, ultimately support more robust clinical decision-making in the early identification of angina pectoris among patients with diabetes.