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.

With the increasing use of plastics worldwide, the amount of plastic waste being discarded has also risen. This plastic waste undergoes physical and chemical processes, breaking down into smaller particles known as microplastics (MPs) or nanoplastics (NPs). Advances in technology have enhanced our ability to detect these smaller particles, and it has been confirmed that plastics can be found in marine organisms as well as within the human body. However, research on the effects of MPs or NPs on living organisms has only recently been started, and our understanding remains limited. Studies on the immunological impacts are still ongoing, revealing that MPs and NPs can differentially affect various immune cells based on the material, size, and shape of the plastic particles. In this review, we aim to provide a comprehensive understanding of the effects of MPs and NPs on the immune system. We will also explore the methods for plastic removal through physicochemical, microbial, or biological means.

Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

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.