Objectives: We analyzed the demographic and clinical characteristics of patients diagnosedwith coronavirus disease 2019 (COVID-19), focusing specifically on severe/critical cases, andassessed the trends and rates of severity and fatality among these patients in the Republic of Korea. Methods: Clinical data on patients with COVID-19 from January 20, 2020 to August 30, 2023were collected from the Korea Disease Control and Prevention Agency’s database. We identified patients who progressed to severe/critical conditions and analyzed their demographic and clinical profiles. Severity and fatality rates were calculated and compared annually to track thedisease progression over time. Results: During the surveillance period, 34,572,554 COVID-19 cases were confirmed, among whom 38,112 (0.11%) progressed to severe/critical conditions. Most severe/critical cases occurred in individuals aged ≥60 years, with a notable increase in patients aged ≥80 years from 2022.The overall severity rate was 0.19%, with a fatality rate of 0.10%. However, the severity of cases gradually diminished during the study period. In 2022, the severity and fatality rates decreased to 0.14% and 0.09%, respectively. In 2023, while the severity rate remained stable at 0.15%, thefatality rate further decreased to 0.06%. Notably, throughout the study period, individuals aged ≥80 years had a significantly higher severity rate (2.44%), with a fatality rate of 1.75%. Conclusion These findings underscore the importance of prioritizing protection and management strategies for older adults and high-risk groups to mitigate the impact ofCOVID-19. Continued surveillance and analysis are essential to effectively control COVID-19 and minimize its burden on public health.

Objectives: We analyzed the demographic and clinical characteristics of patients diagnosedwith coronavirus disease 2019 (COVID-19), focusing specifically on severe/critical cases, andassessed the trends and rates of severity and fatality among these patients in the Republic of Korea. Methods: Clinical data on patients with COVID-19 from January 20, 2020 to August 30, 2023were collected from the Korea Disease Control and Prevention Agency’s database. We identified patients who progressed to severe/critical conditions and analyzed their demographic and clinical profiles. Severity and fatality rates were calculated and compared annually to track thedisease progression over time. Results: During the surveillance period, 34,572,554 COVID-19 cases were confirmed, among whom 38,112 (0.11%) progressed to severe/critical conditions. Most severe/critical cases occurred in individuals aged ≥60 years, with a notable increase in patients aged ≥80 years from 2022.The overall severity rate was 0.19%, with a fatality rate of 0.10%. However, the severity of cases gradually diminished during the study period. In 2022, the severity and fatality rates decreased to 0.14% and 0.09%, respectively. In 2023, while the severity rate remained stable at 0.15%, thefatality rate further decreased to 0.06%. Notably, throughout the study period, individuals aged ≥80 years had a significantly higher severity rate (2.44%), with a fatality rate of 1.75%. Conclusion These findings underscore the importance of prioritizing protection and management strategies for older adults and high-risk groups to mitigate the impact ofCOVID-19. Continued surveillance and analysis are essential to effectively control COVID-19 and minimize its burden on public health.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objectives: We analyzed the demographic and clinical characteristics of patients diagnosedwith coronavirus disease 2019 (COVID-19), focusing specifically on severe/critical cases, andassessed the trends and rates of severity and fatality among these patients in the Republic of Korea. Methods: Clinical data on patients with COVID-19 from January 20, 2020 to August 30, 2023were collected from the Korea Disease Control and Prevention Agency’s database. We identified patients who progressed to severe/critical conditions and analyzed their demographic and clinical profiles. Severity and fatality rates were calculated and compared annually to track thedisease progression over time. Results: During the surveillance period, 34,572,554 COVID-19 cases were confirmed, among whom 38,112 (0.11%) progressed to severe/critical conditions. Most severe/critical cases occurred in individuals aged ≥60 years, with a notable increase in patients aged ≥80 years from 2022.The overall severity rate was 0.19%, with a fatality rate of 0.10%. However, the severity of cases gradually diminished during the study period. In 2022, the severity and fatality rates decreased to 0.14% and 0.09%, respectively. In 2023, while the severity rate remained stable at 0.15%, thefatality rate further decreased to 0.06%. Notably, throughout the study period, individuals aged ≥80 years had a significantly higher severity rate (2.44%), with a fatality rate of 1.75%. Conclusion These findings underscore the importance of prioritizing protection and management strategies for older adults and high-risk groups to mitigate the impact ofCOVID-19. Continued surveillance and analysis are essential to effectively control COVID-19 and minimize its burden on public health.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.