Background: An international reference measurement laboratory network for creatinine (Cr) is lacking; therefore, Korea developed an independent evaluation and certification system. The in vitro diagnostics (IVD) certification program, launched in 2017, formed part of a broader Cr standardization initiative intended to enhance accuracy at the manufacturing stage. Methods: The program was designed to evaluate analytical systems, including all reagent lots, calibrators, and instrument models, twice annually. Bias, imprecision, total error (TE), and linearity were evaluated based on established acceptance criteria. A post-certification process allows submission for a second challenge and validation of corrective actions. Results: Between 2017 and 2023, 489 analytical systems were evaluated. Average acceptance rates for bias, imprecision, TE, and linearity were 70.8%, 95.9%, 87.7%, and 87.8%, respectively. The lowest acceptance rate for bias evaluation was 8.7% for the kinetic Jaffe method without compensation in 2018. Over the 7-year period, the mean absolute percentage bias (absBias%), coefficient of variation (CV), and TE were 4.62%, 1.37%,and 7.29%, respectively. The highest absBias% (7.94%) was observed in the 0.0 ≤ Cr < 1.0target value range. Since 2019, a consistent reduction in absBias% has been observed. Conclusions This program is a pioneering response to the absence of a global certification program for Cr assays. It offers significant advantages, including comprehensive evaluations, fee-free participation, and a robust post-certification process. Continuous participation and improvement efforts by manufacturers have contributed to enhanced accuracy in Cr assays.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Purpose: Compassion is integral to nursing, yet the concept of compassionate care is not thoroughly understood. This study aimed to clarify the nature of compassionate care among Korean clinical nurses using a hybrid model. Methods: This study utilized a mixed methods approach involving hybrid concept analysis to explore the nature and attributes of compassionate care, encompassing both theoretical and empirical stages. In the theoretical stage, the domains, attributes, and a preliminary definition of compassionate care were formulated. In the fieldwork stage, in-depth interviews with 18 nurses were conducted to gather insights, which were integrated in the final stage. Results: Compassionate care was categorized into three domains: cognitive, relational, and behavioral, with 11 defining attributes. It was defined as the capacity to recognize individual patient needs, engage empathetically with their suffering, establish trust through emotional connection, and deliver therapeutic partnership, specialized, personalized, ethical, and holistic care. Conclusion This study advances the understanding of compassionate care in nursing by providing a multidimensional framework. It lays the groundwork for future research and practical applications, emphasizing the need for measurement tools and strategies to promote compassionate care among clinical nurses.

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 increasing number of people living with human immunodeficiency virus (HIV) in Korea has prompted interest in using the national surveillance system as a database for studying their health. To investigate the relationships between socio-demographic and epidemiological characteristics and mortality rates, a nationwide retrospective cohort was formed by integrating surveillance data with the Cause of Death Statistics from Statistics Korea. This integration included incidence reports, epidemiological investigations, and death reports from the surveillance data, enriched with detailed mortality information from the Cause of Death data. The cohort comprised 17,199 adult Korean individuals diagnosed with HIV infection from 1985 to 2020. By the end of 2020, 2,721 of these individuals were confirmed deceased. The sex ratio of the study participants was 14.3:1.0 (male to female), with 78.2% being under 50 years old at the time of diagnosis. Sexual contact was identified as the primary transmission route, accounting for 75.7% of cases. HIV disease emerged as the predominant cause of death, representing two-thirds (1,817 of 2,721) of the fatalities, followed by injuries and trauma, malignancies, and cardiovascular diseases. Recommendations for further cohort studies may be submitted to the Korea Disease Control and Prevention Agency.

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.