Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: The study aims to confirm the mediating effect of nursing professional pride in the relationship between nursing practice environment, nursing performance, and retention intention. Methods: A descriptive cross-sectional survey was conducted from December 13 to 31, 2021, involving 127 nurses. The following statistical analysis was conducted: t-test, ANOVA, Scheffé test, Pearson's correlation coefficient analysis, and Hayes Process Macro Model 4 (to test the mediating effect). Results: Nursing practice environment showed a significant positive correlation with nursing performance, retention intention, and nursing professional pride. Nursing practice performance showed a positive correlation with retention intention and nursing professional pride, and retention intention showed a significant positive correlation with nursing professional pride. The mediating effect of nursing professional pride was found in the effect of nurses' nursing practice environment on their retention intention. In addition, the mediating effect of nursing professional pride was found in the effect of nurses' nursing practice performance on their retention intention. Conclusion Through this study, it was confirmed that nursing professional pride is a major A factor affecting retention intention in the hospital. Therefore, in order to increase nurses' retention intention to remain in Hospital, the basis of basic data was presented for strategy development.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Gastric cancer is one of the most common cancers in Korea and the world. Since 2004, this is the 4th gastric cancer guideline published in Korea which is the revised version of previous evidence-based approach in 2018. Current guideline is a collaborative work of the interdisciplinary working group including experts in the field of gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology and guideline development methodology. Total of 33 key questions were updated or proposed after a collaborative review by the working group and 40 statements were developed according to the systematic review using the MEDLINE, Embase, Cochrane Library and KoreaMed database. The level of evidence and the grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation proposition. Evidence level, benefit, harm, and clinical applicability was considered as the significant factors for recommendation. The working group reviewed recommendations and discussed for consensus. In the earlier part, general consideration discusses screening, diagnosis and staging of endoscopy, pathology, radiology, and nuclear medicine. Flowchart is depicted with statements which is supported by meta-analysis and references. Since clinical trial and systematic review was not suitable for postoperative oncologic and nutritional follow-up, working group agreed to conduct a nationwide survey investigating the clinical practice of all tertiary or general hospitals in Korea. The purpose of this survey was to provide baseline information on follow up. Herein we present a multidisciplinary-evidence based gastric cancer guideline.