This study aimed to derive insights into professional self-concept, teamwork, and job satisfaction among emergency department (ED) nurses and to prepare basic data for improving job satisfaction among ED nurses. Methods: The study was conducted between October 2023 and April 2024 at two certified tertiary hospitals and one general hospital in Gyeonggi-do, Korea, among 124 ED nurses. Data were analyzed using SPSS version 26.0, and the analysis included descriptive statistics, the t-test, Pearson correlation, and multiple regression. Results: The factors affecting job satisfaction in ED nurses were professional self-concept (β = .66, p < .001), teamwork (β = .17, p = .003), and ED work satisfaction (β = .17, p = .003), and the overall explanatory power of the model was 67.7%. Conclusion: This study makes a significant contribution by demonstrating that professional self-concept and teamwork can increase job satisfaction in ED nurses. Therefore, as a strategy to increase job satisfaction through improving ED nurses’ professional self-concept and teamwork, effort should be made to apply team simulation programs that provide education and self-improvement opportunities for frequent diseases and situations encountered in the ED, and to communicate effectively about patient treatment strategies.

This study aimed to derive insights into professional self-concept, teamwork, and job satisfaction among emergency department (ED) nurses and to prepare basic data for improving job satisfaction among ED nurses. Methods: The study was conducted between October 2023 and April 2024 at two certified tertiary hospitals and one general hospital in Gyeonggi-do, Korea, among 124 ED nurses. Data were analyzed using SPSS version 26.0, and the analysis included descriptive statistics, the t-test, Pearson correlation, and multiple regression. Results: The factors affecting job satisfaction in ED nurses were professional self-concept (β = .66, p < .001), teamwork (β = .17, p = .003), and ED work satisfaction (β = .17, p = .003), and the overall explanatory power of the model was 67.7%. Conclusion: This study makes a significant contribution by demonstrating that professional self-concept and teamwork can increase job satisfaction in ED nurses. Therefore, as a strategy to increase job satisfaction through improving ED nurses’ professional self-concept and teamwork, effort should be made to apply team simulation programs that provide education and self-improvement opportunities for frequent diseases and situations encountered in the ED, and to communicate effectively about patient treatment strategies.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

This study aimed to derive insights into professional self-concept, teamwork, and job satisfaction among emergency department (ED) nurses and to prepare basic data for improving job satisfaction among ED nurses. Methods: The study was conducted between October 2023 and April 2024 at two certified tertiary hospitals and one general hospital in Gyeonggi-do, Korea, among 124 ED nurses. Data were analyzed using SPSS version 26.0, and the analysis included descriptive statistics, the t-test, Pearson correlation, and multiple regression. Results: The factors affecting job satisfaction in ED nurses were professional self-concept (β = .66, p < .001), teamwork (β = .17, p = .003), and ED work satisfaction (β = .17, p = .003), and the overall explanatory power of the model was 67.7%. Conclusion: This study makes a significant contribution by demonstrating that professional self-concept and teamwork can increase job satisfaction in ED nurses. Therefore, as a strategy to increase job satisfaction through improving ED nurses’ professional self-concept and teamwork, effort should be made to apply team simulation programs that provide education and self-improvement opportunities for frequent diseases and situations encountered in the ED, and to communicate effectively about patient treatment strategies.

This study aimed to derive insights into professional self-concept, teamwork, and job satisfaction among emergency department (ED) nurses and to prepare basic data for improving job satisfaction among ED nurses. Methods: The study was conducted between October 2023 and April 2024 at two certified tertiary hospitals and one general hospital in Gyeonggi-do, Korea, among 124 ED nurses. Data were analyzed using SPSS version 26.0, and the analysis included descriptive statistics, the t-test, Pearson correlation, and multiple regression. Results: The factors affecting job satisfaction in ED nurses were professional self-concept (β = .66, p < .001), teamwork (β = .17, p = .003), and ED work satisfaction (β = .17, p = .003), and the overall explanatory power of the model was 67.7%. Conclusion: This study makes a significant contribution by demonstrating that professional self-concept and teamwork can increase job satisfaction in ED nurses. Therefore, as a strategy to increase job satisfaction through improving ED nurses’ professional self-concept and teamwork, effort should be made to apply team simulation programs that provide education and self-improvement opportunities for frequent diseases and situations encountered in the ED, and to communicate effectively about patient treatment strategies.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.