Background: Skin rejuvenation has become an increasingly popular noninvasive approach to address age-related changes such as sagging, wrinkles, and skin laxity. Energy-based devices (EBDs) and injectables are widely used, but their application requires careful customization based on individual patient characteristics to optimize outcomes and minimize potential adverse effects. Objective: This study aimed to explore clinical practice patterns among board-certified dermatologists in South Korea, focusing on their strategies for tailoring skin rejuvenation treatments to individual patients, including the integration of EBDs, injectables, and senotherapeutics. Methods: A structured survey comprising 10 questions was administered to 13 experienced dermatologists specializing in skin rejuvenation. The survey covered treatment strategies for patients with varying facial fat volumes, pain management approaches, and the use of EBDs, injectables and senotherapeutics. Results: High-intensity focused ultrasound (HIFU) and radiofrequency (RF) were the most employed EBDs, often combined with injectables for enhanced outcomes. For patients with higher facial fat, HIFU and deoxycholic acid injections were preferred for contouring and tightening. For those with lower facial fat, biostimulatory agents such as poly-D, L-lactic acid and microneedle RF were favored to restore volume and elasticity. Pain management strategies included topical anesthetics and stepwise protocols. Although less commonly used, senotherapeutics were occasionally prescribed for specific conditions, such as melasma and extensive photoaging. Conclusion Dermatologists in South Korea employ a variety of patient-specific strategies for skin rejuvenation, combining various EBDs, injectables, and senotherapeutics. These findings highlight the importance of personalized treatment protocols and the need for further research to optimize treatment efficacy and safety.

Background: This study aimed to develop standard job categories for constructing a job-exposure matrix (JEM) for police officers in South Korea and to evaluate their applicability. Methods: We examined standard job codes related to police personnel management and compared them with job classifications from police publications. Using R Shiny, we developed a web-based search tool for standard codes. A pilot survey of 130 police officers assessed the codes' applicability and relevance to health-related hazardous factors. Results: Eighty-seven standard functional codes used in the police personnel management system POOL were organized into minor categories as the basic units of standard jobs. These were grouped into 20 sub-major categories and further consolidated into 10 major categories to develop the standard job codes. The responses to the standard job codes in the pilot survey were 75% accurate compared with the final expert evaluation results and 99.2% accurate compared with the algorithm-based automatic allocation results. The results of the job-hazardous factor network analysis revealed that the most frequently reported hazardous factor was emotional labor, followed by night shifts and electromagnetic waves. Emotional labor was identified as the top hazardous factor in six out of the nine standard job categories. Conclusions The standard job codes developed in this study were designed in connection with the personnel management system for police officers, making them well-suited for constructing a comprehensive JEM for the entire police force.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: The increasing incidence and mortality rates of tuberculosis among older individuals who suffer from multiple morbidities and are vulnerable to malnutrition are major obstacles to efforts to eradicate tuberculosis in the Republic of Korea. Herein, we identified the factors associated with mortality during anti-tuberculosis treatment in patients with pulmonary tuberculosis. Methods: We conducted a case-control study and extracted data from the database of a multi-center prospective observational cohort study in Korea. Among the participants with rifampicin-susceptible pulmonary tuberculosis, the survival group was defined as those who successfully completed treatment within one year, whereas the mortality group was defined as those who died during treatment. Univariable and multivariable logistic regression analyses were performed to identify factors associated with TB mortality. Results: Among 1,119 participants with pulmonary TB registered between 2019 and 2021, 799 and 59 were grouped in the survival and mortality groups, respectively. Age, positive smear results, alarming symptoms, nutrition risk score, Charlson comorbidity index score, and initial standard treatment regimen were significant based on univariable analysis and were selected for the multivariable logistic regression model. Nutrition risk score (adjusted odds ratio, 2.44; 95% confidence interval, 1.72–3.48) and Charlson comorbidity index score (adjusted odds ratio, 1.62; 95% confidence interval, 1.35–1.94) remained statistically significant in the multivariate analysis. Conclusion Nutritional status and comorbidities at baseline were identified as important factors associated with mortality in patients with pulmonary tuberculosis.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: The increasing incidence and mortality rates of tuberculosis among older individuals who suffer from multiple morbidities and are vulnerable to malnutrition are major obstacles to efforts to eradicate tuberculosis in the Republic of Korea. Herein, we identified the factors associated with mortality during anti-tuberculosis treatment in patients with pulmonary tuberculosis. Methods: We conducted a case-control study and extracted data from the database of a multi-center prospective observational cohort study in Korea. Among the participants with rifampicin-susceptible pulmonary tuberculosis, the survival group was defined as those who successfully completed treatment within one year, whereas the mortality group was defined as those who died during treatment. Univariable and multivariable logistic regression analyses were performed to identify factors associated with TB mortality. Results: Among 1,119 participants with pulmonary TB registered between 2019 and 2021, 799 and 59 were grouped in the survival and mortality groups, respectively. Age, positive smear results, alarming symptoms, nutrition risk score, Charlson comorbidity index score, and initial standard treatment regimen were significant based on univariable analysis and were selected for the multivariable logistic regression model. Nutrition risk score (adjusted odds ratio, 2.44; 95% confidence interval, 1.72–3.48) and Charlson comorbidity index score (adjusted odds ratio, 1.62; 95% confidence interval, 1.35–1.94) remained statistically significant in the multivariate analysis. Conclusion Nutritional status and comorbidities at baseline were identified as important factors associated with mortality in patients with pulmonary tuberculosis.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

Purpose: This study aimed to define an optimal age cutoff for early-onset gastric cancer (EOGC) and compare its characteristics with those of late-onset gastric cancer (LOGC) using nationwide survey data. Methods: Using data from a nationwide survey, this comprehensive population-based study analyzed data spanning 3 years (2009, 2014, and 2019). The joinpoint analysis and interrupted time series (ITS) methodology were employed to identify age cutoffs for EOGC based on the sex ratio and tumor histology. Clinicopathologic characteristics and surgical outcomes were compared between the EOGC and LOGC groups. Results: The age cutoff for defining EOGC was suggested to be 50 years, supported by joinpoint and ITS analyses. Early gastric cancer was predominantly present in the EOGC and LOGC groups. Patients with EOGC comprised 20.3% of the total study cohort and demonstrated a more advanced disease stage compared to patients with LOGC. However, patients with EOGC underwent more minimally invasive surgeries, experienced shorter hospital stays, and had lower postoperative morbidity and mortality rates. Conclusion This study proposes an age of ≤50 years as a criterion for defining EOGC and highlights its features compared to LOGC. Further research using this criterion should guide tailored treatment strategies and improve outcomes for young patients with gastric cancer.