Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: The hospitalist system, formally adopted in South Korea in 2021 after a pilot program in 2016, has been associated with improvements in inpatient care outcomes and patient satisfaction. However, a persistent shortage of hospitalists— recently worsened by increasing demands on inpatient care—has raised concerns regarding workforce stability. This study aimed to compare job satisfaction and its determinants between medical and surgical hospitalists in South Korea. Methods: A nationwide cross-sectional online survey was conducted in February 2024 among 389 board-certified hospitalists registered with the Korean Society of Hospital Medicine and the Korean Society of Surgical Hospital Medicine.The survey included questions on demographics, work environment, job satisfaction (monetary and nonmonetary), and career intentions. Statistical analyses included chi-square tests, Mann-Whitney U-tests, and multiple linear regression. Results: A total of 94 hospitalists responded (67 medical and 27 surgical). Surgical hospitalists reported significantly longer weekly working hours (47.9 hours vs. 40.9 hours, P = 0.013) and higher patient loads (19.5 patients vs. 15.4 patients, P = 0.003). Despite these differences, overall satisfaction levels were similar between the groups. Eligibility for faculty appointment and availability of research and education funding were significantly associated with nonmonetary satisfaction. Annual salary was the most significant predictor of monetary satisfaction, explaining 17.2% of the variance. Conclusion Surgical hospitalists experienced higher workloads but maintained comparable satisfaction levels to their medical counterparts. Enhancing academic opportunities and tailoring financial incentives may be effective strategies to improve job satisfaction and support workforce retention across specialties.

Children face the excitement of a changing world but also encounter environmental threats to their health that were neither known nor suspected several decades ago. Children are at particular risk of exposure to pollutants that are widely dispersed in the air, water, and food. Children and adolescents are exposed to chemical, physical, and biological risks at home, in school, and elsewhere. Actions are needed to reduce these risks for children exposed to a series of environmental hazards. Exposure to a number of persistent environmental pollutants including air pollutants, endocrine disruptors, noise, electromagnetic waves (EMWs), tobacco and other noxious substances, heavy metals, and microplastics, is linked to damage to the nervous and immune systems and affects reproductive function and development. Exposure to environmental hazards is responsible for several acute and chronic diseases that have replaced infectious diseases as the principal cause of illnesses and death during childhood. Children are disproportionately exposed to environmental toxicities. Children drink more water, eat more food, and breathe more frequently than adults. As a result, children have a substantially heavier exposure to toxins present in water, food, or air than adults. In addition, their hand-to-mouth behaviors and the fact that they live and play close to the ground make them more vulnerable than adults. Children undergo rapid growth and development processes that are easily disrupted. These systems are very delicate and cannot adequately repair thetional development in children’s environmental health was the Declaration of the Environment Leaders of the Eight on Children’s Environmental Health by the Group of Eight. In 2002, the World Health Organization launched an initiative to improve children’s environmental protection effort. Here, we review major environmental pollutants and related hazards among children and adolescents.

Children face the excitement of a changing world but also encounter environmental threats to their health that were neither known nor suspected several decades ago. Children are at particular risk of exposure to pollutants that are widely dispersed in the air, water, and food. Children and adolescents are exposed to chemical, physical, and biological risks at home, in school, and elsewhere. Actions are needed to reduce these risks for children exposed to a series of environmental hazards. Exposure to a number of persistent environmental pollutants including air pollutants, endocrine disruptors, noise, electromagnetic waves (EMWs), tobacco and other noxious substances, heavy metals, and microplastics, is linked to damage to the nervous and immune systems and affects reproductive function and development. Exposure to environmental hazards is responsible for several acute and chronic diseases that have replaced infectious diseases as the principal cause of illnesses and death during childhood. Children are disproportionately exposed to environmental toxicities. Children drink more water, eat more food, and breathe more frequently than adults. As a result, children have a substantially heavier exposure to toxins present in water, food, or air than adults. In addition, their hand-to-mouth behaviors and the fact that they live and play close to the ground make them more vulnerable than adults. Children undergo rapid growth and development processes that are easily disrupted. These systems are very delicate and cannot adequately repair thetional development in children’s environmental health was the Declaration of the Environment Leaders of the Eight on Children’s Environmental Health by the Group of Eight. In 2002, the World Health Organization launched an initiative to improve children’s environmental protection effort. Here, we review major environmental pollutants and related hazards among children and adolescents.

Children face the excitement of a changing world but also encounter environmental threats to their health that were neither known nor suspected several decades ago. Children are at particular risk of exposure to pollutants that are widely dispersed in the air, water, and food. Children and adolescents are exposed to chemical, physical, and biological risks at home, in school, and elsewhere. Actions are needed to reduce these risks for children exposed to a series of environmental hazards. Exposure to a number of persistent environmental pollutants including air pollutants, endocrine disruptors, noise, electromagnetic waves (EMWs), tobacco and other noxious substances, heavy metals, and microplastics, is linked to damage to the nervous and immune systems and affects reproductive function and development. Exposure to environmental hazards is responsible for several acute and chronic diseases that have replaced infectious diseases as the principal cause of illnesses and death during childhood. Children are disproportionately exposed to environmental toxicities. Children drink more water, eat more food, and breathe more frequently than adults. As a result, children have a substantially heavier exposure to toxins present in water, food, or air than adults. In addition, their hand-to-mouth behaviors and the fact that they live and play close to the ground make them more vulnerable than adults. Children undergo rapid growth and development processes that are easily disrupted. These systems are very delicate and cannot adequately repair thetional development in children’s environmental health was the Declaration of the Environment Leaders of the Eight on Children’s Environmental Health by the Group of Eight. In 2002, the World Health Organization launched an initiative to improve children’s environmental protection effort. Here, we review major environmental pollutants and related hazards among children and adolescents.

Children face the excitement of a changing world but also encounter environmental threats to their health that were neither known nor suspected several decades ago. Children are at particular risk of exposure to pollutants that are widely dispersed in the air, water, and food. Children and adolescents are exposed to chemical, physical, and biological risks at home, in school, and elsewhere. Actions are needed to reduce these risks for children exposed to a series of environmental hazards. Exposure to a number of persistent environmental pollutants including air pollutants, endocrine disruptors, noise, electromagnetic waves (EMWs), tobacco and other noxious substances, heavy metals, and microplastics, is linked to damage to the nervous and immune systems and affects reproductive function and development. Exposure to environmental hazards is responsible for several acute and chronic diseases that have replaced infectious diseases as the principal cause of illnesses and death during childhood. Children are disproportionately exposed to environmental toxicities. Children drink more water, eat more food, and breathe more frequently than adults. As a result, children have a substantially heavier exposure to toxins present in water, food, or air than adults. In addition, their hand-to-mouth behaviors and the fact that they live and play close to the ground make them more vulnerable than adults. Children undergo rapid growth and development processes that are easily disrupted. These systems are very delicate and cannot adequately repair thetional development in children’s environmental health was the Declaration of the Environment Leaders of the Eight on Children’s Environmental Health by the Group of Eight. In 2002, the World Health Organization launched an initiative to improve children’s environmental protection effort. Here, we review major environmental pollutants and related hazards among children and adolescents.

Background: /Aim: Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT). Methods: We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1. Results: A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74). Conclusion The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.