Purpose: This study aimed to explore the practices, perceptions, and barriers related to specialty palliative care (SPC) referrals among oncologists in Korea, highlighting the clinical implications of early integration. Materials and Methods: A cross-sectional online survey targeting board-certified hemato-oncology specialists was conducted between August 1-25, 2024. The survey assessed referral practices, attitudes toward early SPC integration, referral criteria, barriers, and institutional characteristics. Results: A total of 227 oncologists participated (response rate, 36.7%). Among them, 68.7% reported frequent SPC referrals, with higher referral rates observed among younger physicians, those in tertiary hospitals, and institutions with in-house SPC teams (p < 0.001). Although 74.9% supported early SPC integration, referrals were often inconsistently timed, frequently occurring after disease progression or at the discontinuation of chemotherapy. For time-based referrals, the most commonly endorsed triggers were disease progression despite palliative second-line treatment and a prognosis of expected mortality within 6-12 months. Need-based referral triggers such as patient or family requests (96.5%), psychological distress (89.9%), or uncontrolled symptoms (83.3%), were also widely endorsed. The major barriers to early SPC integration included patient and family resistance (70.0%) and limited availability of SPC teams (34.4%). Conclusion This study emphasizes the importance of systematic efforts to promote timely SPC integration in Korea, including education to raise patient awareness, improved referral systems, and enhanced infrastructure. The positive attitudes toward early SPC among oncologists reflect a growing recognition of its value, highlighting the need for strategies that align with international standards.

Coronavirus disease 2019 (COVID-19) generally presents with milder illness in children and adolescents than in adults; however, infants and those with underlying chronic conditions, obesity, or immunocompromised states remain at increased risk for severe dis-ease and death. Multisystem inflammatory syndrome in children (MIS-C) affects multiple organs, including the heart, gastrointestinaltract, and skin, and can result in severe illness requiring hospitalization or death. In children and adolescents, long COVID manifests with age-dependent, heterogeneous symptoms, leading not only to persistent physical complaints but also to neurologic manifestations and mental health problems. Therefore, evidence-based clinical guidelines and approved treatments for COVID-19 in children and adolescents remain limited, and research is needed to build a pediatric evidence base. Generating high-quality data is essential to develop optimized diagnostic and therapeutic strategies, establish standardized care pathways, and ultimately improve preparedness and outcomes for children and adolescents during future emerging infectious disease outbreaks. In this clinical information article, we review the epidemiology, clinical symptoms, complications, and drug treatment of COVID-19 in the pediatric population.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: This study aimed to assess the real-world clinical outcomes of consolidative durvalumab in patients with unresectable locally advanced non–small cell lung cancer (LA-NSCLC) and to explore the role of radiotherapy in the era of immunotherapy. Materials and Methods: This retrospective study assessed 171 patients with unresectable LA-NSCLC who underwent concurrent chemoradiotherapy (CCRT) with or without consolidative durvalumab at Asan Medical Center between May 2018 and May 2021. Primary outcomes included freedom from locoregional failure (FFLRF), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS). Results: Durvalumab following CCRT demonstrated a prolonged median PFS of 20.9 months (p=0.048) and a 3-year FFLRF rate of 57.3% (p=0.008), compared to 13.7 months and 38.8%, respectively, with CCRT alone. Furthermore, the incidence of in-field recurrence was significantly greater in the CCRT-alone group compared to the durvalumab group (26.8% vs. 12.4%, p=0.027). While median OS was not reached with durvalumab, it was 35.4 months in patients receiving CCRT alone (p=0.010). Patients positive for programmed cell death ligand 1 (PD-L1) expression showed notably better outcomes, including FFLRF, DMFS, PFS, and OS. Adherence to PACIFIC trial eligibility criteria identified 100 patients (58.5%) as ineligible. The use of durvalumab demonstrated better survival regardless of eligibility criteria. Conclusion The use of durvalumab consolidation following CCRT significantly enhanced locoregional control and OS in patients with unresectable LA-NSCLC, especially in those with PD-L1–positive tumors, thereby validating the role of durvalumab in standard care.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Hypoxaemia is a significant adverse event during endoscopic retrograde cholangiopancreatography (ERCP) under monitored anaesthesia care (MAC); however, no model has been developed to predict hypoxaemia. We aimed to develop and compare logistic regression (LR) and machine learning (ML) models to predict hypoxaemia during ERCP under MAC. Materials and Methods: We collected patient data from our institutional ERCP database. The study population was randomly divided into training and test sets (7:3). Models were fit to training data and evaluated on unseen test data. The training set was further split into k-fold (k=5) for tuning hyperparameters, such as feature selection and early stopping. Models were trained over k loops; the i-th fold was set aside as a validation set in the i-th loop. Model performance was measured using area under the curve (AUC). Results: We identified 6114 cases of ERCP under MAC, with a total hypoxaemia rate of 5.9%. The LR model was established by combining eight variables and had a test AUC of 0.693. The ML and LR models were evaluated on 30 independent data splits. The average test AUC for LR was 0.7230, which improved to 0.7336 by adding eight more variables with an l 1 regularisation-based selection technique and ensembling the LRs and gradient boosting algorithm (GBM). The high-risk group was discriminated using the GBM ensemble model, with a sensitivity and specificity of 63.6% and 72.2%, respectively. Conclusion We established GBM ensemble model and LR model for risk prediction, which demonstrated good potential for preventing hypoxaemia during ERCP under MAC.