Purpose: This study was performed to predict individualized treatment strategies in ruptured abdominal aortic aneurysm (rAAA) by estimating the survival benefit of endovascular aneurysm repair (EVAR) and open surgical repair (OSR) based on anatomical and physiological features using a causal inference model. Methods: This retrospective study included 45 patients with de novo rAAA who underwent EVAR or OSR between 2012 and 2024. Thirty-three variables were analyzed. The model estimated individualized treatment effects (ITE) for 30-day survival.Model interpretability was assessed using Shapley Additive Explanations (SHAP) analysis. Five-fold cross-validation, receiver operating characteristic (ROC) analysis, and calibration plots were used for model evaluation. A clinical decision tree was developed to derive simplified decision rules. Results: The mean ITE was 0.22 ± 0.42, with 33% of patients classified as OSR-benefit candidates. SHAP analysis revealed that suprarenal angle, infrarenal angle, iliac anatomy, and proximal neck characteristics strongly influenced treatment effects. However, some predictors, such as low hemoglobin and systolic blood pressure favoring OSR, conflicted with clinical intuition. ROC analysis showed an area under the curve of 1.00, but calibration suggested overfitting due to a small sample size. Treatment-matched patients had a higher 30-day mortality rate than mismatched patients, suggesting potential bias or unmeasured confounding. The decision tree identified clinically relevant features but displayed structural inconsistencies and impractical cutoff values due to the limited sample size. Conclusion The X-learner model demonstrated the feasibility of individualized treatment prediction in rAAA but suffered from overfitting and limited generalizability. Validation with larger multicenter cohorts is necessary to confirm clinical applicability.

Purpose: To develop the 2025 update to the Korean colorectal cancer (CRC) screening guidelines by systematically evaluating recent evidence, integrating domestic data, and addressing changes since the 2015 guideline revision, thereby providing an evidence-based standard for clinicians and policymakers. Methods: A multidisciplinary committee developed the guidelines using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The process included formulation of three key questions addressing screening efficacy, diagnostic accuracy, and optimal screening age and interval. A systematic review of international guidelines and primary literature was conducted, yielding 327 eligible studies. In addition, a utility-based analysis using a Markov model was performed to determine optimal screening ages and intervals. Results: The evidence synthesis identified high-certainty evidence supporting the use of the fecal immunochemical test (FIT) for reducing CRC mortality and moderate-certainty evidence for colonoscopy. Evidence for computed tomographic colonography (CTC) and stool DNA testing was rated as very low certainty. Based on the evidence review and cost-utility analysis, the committee conditionally recommends CRC screening for asymptomatic, average-risk adults aged 45–74 years using either colonoscopy every 10 years or FIT every 1–2 years. CTC and stool DNA testing were not recommended owing to insufficient evidence. Conclusion The 2025 Korean Guidelines for Colorectal Cancer Screening present updated, evidence-based recommendations tailored to the domestic healthcare context. By conditionally endorsing both colonoscopy and FIT for individuals aged 45–74 years, these guidelines aim to improve population-level screening effectiveness and reduce the burden of CRC in South Korea.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

The Occupational and Environmental Health Screening Cohort of Yangsan Korea (OEC-YK) was established to monitor long-term health outcomes in workers and environmental high-risk citizens through systematic periodic health examinations. The cohort integrates 623,402 examination instances from 105,768 participants at Pusan National University Yangsan Hospital (2012–2023), encompassing general health checkups, occupational exposure surveillance, cancer screenings, and others including asbestos-related surveillance. Examination types included general health examination (32.4%), cancer screening (20.6%), special health examination (16.2%), night-shift work examination (16.0%), chronic disease screening (4.8%), pre-placement examinations (2.1%), and asbestos-related surveillance (3.6%). At baseline, 59.0% were male and 75.5% were aged between 20 and 59 years. Follow-up rates reached 35.0% at 1 year, 31.9% at 2 years, and 3.2% at 11 years. Notably, the inclusion of systematic asbestos examinations, combined with a national asbestos job exposure matrix, allows detailed study of long-latency occupational diseases. This large-scale longitudinal dataset supports exposure-disease linkage analysis, job-exposure integration, and time-series modeling of worker health trajectories in Korea.

Background: This study aimed to develop prediction models for the work-relatedness of shoulder diseases through machine learning algorithms. Methods: The dataset comprised 7,270 cases of 8,302 individuals who applied for occupational diseases and received the final approval decision from the Korea Workers' Compensation and Welfare Service's Disease Evaluation Committee, which is related to shoulder musculoskeletal disorders between January 2020 and December 2021. In this study, demographic analysis and difference of approval rate by shoulder diseases were performed. Additionally, machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, and the XGBoost, were utilized to construct prediction models for work-relatedness assessment. Results: The performance of each model was evaluated. XGBoost showed an accuracy of 81.64% and an area under the curve of 0.73, and random forest showed an accuracy of 84.46% and an area under the curve of 0.73. Key factors influencing work-relatedness assessment were employment period, physical burden score, gender, and age. Conclusion The application of various machine learning techniques showed high performance score, representing that it would be helpful to reduce the differences in judgment between occupational environment medicine physicians.

Background: A job-exposure matrix (JEM) is an important surrogate indicator to evaluate past exposure levels. Although a Korean asbestos JEM has been constructed previously, this JEM includes only a few industrial and occupational groups. This study aimed to reconstruct the JEM by integrating the latest organized data to improve its utility. Methods: We used recent Korean standard industry and occupation codes and extracted 36 articles from a systematic literature review to initiate the reconstruction of the previous Korean asbestos JEM. The resulting data consisted of 141 combinations of industrial and occupational groups. Data from the Netherlands's JEM were also reviewed and categorized into 70 industrial and 117 occupational groups by matching with the Korean data. We also utilized Germany's data, which consisted of 10 industrial and 14 occupational groups. Results: The reconstructed Korean asbestos JEM had 141 combinations of industries and occupations. The time periods are from the 1980s to the 2000s in 10-year intervals. Most of the data were distributed between the 1990s and the 2000s. Occupations with high exposure to asbestos included knitting and weaving machine operators, automobile mechanics or assemblers, ship mechanics or assemblers, mineral ore and stone products processing mechanics, and metal casting machine operators or mold makers. Conclusions The reconstructed Korean asbestos JEM has expanded the type and duration of the occupational groups of the previous JEM and can serve as an important reference tool for evaluating asbestos exposure and designing compensation and prevention policies in Korea.