Purpose: The male predominance in the incidence of gastric cancer (GC) is established; however, sex differences in the prognosis of GC remain controversial. As such, this study analyzed the prognosis of patients with GC based on age and sex. Materials and Methods: Data from 14,739 patients diagnosed with GC at Seoul National University Bundang Hospital between 2003 and 2023 were analyzed. Baseline characteristics, histological types of GC, overall and GC-specific survival rates (age and stage stratification), and associated risk factors were analyzed. Results: Females were significantly younger (p < 0.001) and exhibited more gastric body cancers (p < 0.001) and tumors with diffuse-type or poorly differentiated histology (p < 0.001) than males. Females exhibited an advantage over males in terms of overall survival (p=0.004), but not in GC-specific survival. However, age stratification revealed significant sex differences, that females < 50 years of age exhibited survival disadvantages (p < 0.001); however, this trend was reversed with age, and females > 60 years exhibited survival advantages (p < 0.001) for both overall and GC-specific survival. This may be explained by the lower ratio of diffuse-type GC as females age. Furthermore, in the analysis according to stage, females with stage IV disease exhibited significant survival disadvantages, with significantly younger age and a higher proportion of diffuse-type GC which exhibits aggressive features, resulting in poorer survival than in males. Conclusion Age and stage stratification revealed significant differences in survival between the sexes, which can be helpful for public health strategies.

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.

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.

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.

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.

Purpose: To report the findings of a perception survey on intestinal malrotation conducted by the Korean Association of Pediatric Surgeons (KAPS) in 2021. Methods: The perceptions on intestinal malrotation regarding clinical decision making of the KAPS members were collected through web-based survey. Results: A total of 22 surgeons were answered for this study. The results were presented and discussed at the 37th annual meeting of KAPS, which was held in Seoul on June 18, 2021. Conclusion This study provides the clinical decisions of the KAPS members on the intestinal malrotation. The study is expected to be an important reference for improving pediatric surgeons’ understanding and treatment of intestinal malrotation.

Purpose: Diffuse sclerosing variant papillary thyroid carcinoma (DSV-PTC) is a rare and aggressive subtype of PTC.Preoperative diagnosis is challenging owing to the overlapping of imaging characteristics with those of other thyroid conditions. This study aimed to evaluate the accuracy of preoperative ultrasound (US) in predicting DSV-PTC and to identify significant diagnostic factors. Methods: This retrospective study analyzed 34 patients who were preoperatively suspected of having DSV-PTC based on US findings and later underwent thyroidectomy. Patients were divided into 2 groups based on the final histopathological diagnosis: DSV-PTC and non-DSV-PTC. Demographic, radiological, and pathological characteristics were also compared. Results: Only 32.4% of patients initially suspected of having DSV-PTC were confirmed postoperatively. Among the US features, the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) score 5 was significantly associated with DSV-PTC (P = 0.038), whereas other radiological factors, including echogenicity and microcalcifications, were not. The histopathological features, such as tumor size, BRAF and TERT mutations, vascular invasion, and lymph node metastasis, showed no significant differences between the groups. Conclusion Preoperative US has limited accuracy (32.4%) in diagnosing DSV-PTC. Because of the aggressive treatment recommendations based on preoperative suspicion, clinicians should carefully consider the limitations of imaging. Further studies incorporating fine-needle aspiration or core needle biopsy are required to improve diagnostic accuracy.

Purpose: For neonates admitted to the neonatal intensive care unit (NICU), appropriate nutritional assessment and intervention are important for adequate growth. In this study, we aimed to determine whether there were changes in the nutritional supply and growth status of premature infants hospitalized in the NICU after the introduction of the Nutrition support team (NST). Methods: This study retrospectively analyzed premature infants admitted to the NICU for over 14 days. The average daily calorie, protein, and fat supply at 1 and 2 weeks after birth were compared before and after NST, and growth was evaluated by changes in length, weight, and head circumference z-scores at birth and 28 days after birth. Results: A total of 79 neonates were included in the present study, with 32 in the preNST group and 47 in the post-NST group. The average daily energy supply during the first (p=0.001) and second (p=0.029) weeks postnatal was significantly higher in the post-NST group than in the pre-NST group. Lipid supply for the first week was significantly higher in the post-NST group than in the pre-NST group (p=0.010). The change in the z-score for length was significantly higher in the post-NST group than in the pre-NST group (p=0.049). Conclusion Nutrient supply and length z-score change increased significantly at 28 days after birth in the post-NST group. These results suggest that calorie calculators and NST activity can promote adequate growth and development in neonates.

Purpose: For neonates admitted to the neonatal intensive care unit (NICU), appropriate nutritional assessment and intervention are important for adequate growth. In this study, we aimed to determine whether there were changes in the nutritional supply and growth status of premature infants hospitalized in the NICU after the introduction of the Nutrition support team (NST). Methods: This study retrospectively analyzed premature infants admitted to the NICU for over 14 days. The average daily calorie, protein, and fat supply at 1 and 2 weeks after birth were compared before and after NST, and growth was evaluated by changes in length, weight, and head circumference z-scores at birth and 28 days after birth. Results: A total of 79 neonates were included in the present study, with 32 in the preNST group and 47 in the post-NST group. The average daily energy supply during the first (p=0.001) and second (p=0.029) weeks postnatal was significantly higher in the post-NST group than in the pre-NST group. Lipid supply for the first week was significantly higher in the post-NST group than in the pre-NST group (p=0.010). The change in the z-score for length was significantly higher in the post-NST group than in the pre-NST group (p=0.049). Conclusion Nutrient supply and length z-score change increased significantly at 28 days after birth in the post-NST group. These results suggest that calorie calculators and NST activity can promote adequate growth and development in neonates.

Background: We aimed to investigate the characteristics of pediatric ulcerative colitis (UC) at diagnosis in Korea. Methods: This was a multicenter, registry-based, inception cohort study conducted in Korea between 2021 and 2023. Children and adolescents newly diagnosed with UC < 18 years were included. Baseline clinicodemographics, results from laboratory, endoscopic exams, and Paris classification factors were collected, and associations between factors at diagnosis were investigated. Results: A total 205 patients with UC were included. Male-to-female ratio was 1.59:1, and the median age at diagnosis was 14.7 years (interquartile range 11.9–16.2). Disease extent of E1 comprised 12.2% (25/205), E2 24.9% (51/205), E3 11.2% (23/205), and E4 51.7% (106/205) of the patients. S1 comprised 13.7% (28/205) of the patients. The proportion of patients with a disease severity of S1 was significantly higher in patients with E4 compared to the other groups (E1: 0% vs. E2: 2% vs. E3: 0% vs. E4: 24.5%, P < 0.001). Significant differences between disease extent groups were also observed in Pediatric Ulcerative Colitis Activity Index (median 25 vs. 35 vs. 40 vs. 45, respectively, P < 0.001), hemoglobin (median 13.5 vs.13.2 vs. 11.6 vs. 11.4 g/dL, respectively, P < 0.001), platelet count (median 301 vs. 324 vs. 372 vs. 377 × 103 /μL, respectively, P = 0.001), C-reactive protein (median 0.05 vs. 0.10 vs. 0.17 vs. 0.38 mg/dL, respectively, P < 0.001), and Ulcerative Colitis Endoscopic Index of Severity (median 4 vs. 4 vs. 4 vs. 5, respectively, P = 0.006). No significant differences were observed in factors between groups divided according to sex and diagnosis age. Conclusion This study represents the largest multicenter pediatric inflammatory bowel disease cohort in Korea. Disease severity was associated with disease extent in pediatric patients with UC at diagnosis.