The Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) is widely used for risk stratification in pheochromocytoma and paraganglioma (PPGL), but its clinical utility is limited by inter-observer variability of its parameters and inconsistent predictive performance. Methods: We conducted a multicenter retrospective study of 1,518 patients with PPGL from five tertiary referral centers in Korea. Prognostic utility of PASS system was assessed using logistic regression, Kaplan-Meier analysis, and receiver operating characteristic (ROC) curve analysis. Inter-observer variability was inferred by comparing area under the ROC curve (AUCs) across institutions. Simplified PASS systems were developed based on multivariable analysis of key histopathological parameters. Results: The PASS system was a significant predictor of adverse events and recurrence-free survival. Although the PASS system demonstrated only modest discriminative ability (AUC, 0.673), it showed a high negative predictive value (NPV, 0.885), supporting its usefulness as a screening tool for benign behavior. However, there was significant inter-institutional variability in PASS performance (AUC; range, 0.513 to 0.727; p < .05). The 3-factor Simple PASS, which incorporates necrosis, spindling, and mitotic figures, exhibited less inter-observer variation. The 4-factor Simple PASS, which adds vascular invasion to the 3-factor model, also showed reduced inter-observer variability and improved AUC and NPV compared to the original PASS system. Conclusions: In this multicenter cohort, the PASS system demonstrated high NPV and screening potential, but significant inter-observer variability remains a challenge. Simplification of the PASS system and enhanced pathologist training may improve reproducibility and clinical utility in PPGL risk stratification.

This report presents the case of severe tooth wear and vertical dimension loss in a 71-year-old male patient. A combined conventional and digital approach was employed for full-mouth rehabilitation. After determining an increase in the vertical dimension of 5.5 mm using an anterior jig and diagnostic wax-up, provisional restorations were fabricated and adjusted throughout the adaptation period.For the fabrication of the final prosthesis, digital methodologies such as oral scanning and occlusal acquisition were performed. To obtain precise margin data, a die model was fabricated using the traditional impression method, followed by model scanning, which was then combined with intraoral scan data. The final prosthesis was made of zirconia to enhance esthetics and strength. Consequently, the treatment enhanced both function and esthetics, leading to high patient satisfaction with the outcomes.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.

Background: The optimal duration and net clinical benefit of dual antiplatelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR) have not been elucidated in realworld situations. Methods: Using nationwide claims data from 2013 to 2021, we selected patients who underwent TAVR and categorized them into two groups: short- and long-term (≤ 3 and > 3 months, respectively) DAPT group. Propensity score matching was used to balance baseline characteristics. The primary endpoint was the occurrence of net adverse clinical events (NACEs), including all-cause death, myocardial infarction, stroke, any coronary and peripheral revascularization, systemic thromboembolism, and bleeding events, at 1 year. Survival analyses were conducted using Kaplan-Meier estimation and Cox proportional hazards regression. Results: Patients who met the inclusion criteria (1,695) were selected. Propensity score matching yielded 1,215 pairs of patients: 416 and 799 in the short- and long-term DAPT groups, respectively. In the unmatched cohort, the mean ages were 79.8 ± 6.1 and 79.7 ± 5.8 years for the short- and long-term DAPT groups, respectively. In the matched cohort, the mean ages were 80.6 ± 5.9 and 79.9 ± 5.9 years for the short- and long-term DAPT groups, respectively. Over one year in the unmatched cohort, the NACE incidence was 11.9% and 11.5% in the short- and long-term DAPT groups, respectively (P = 0.893). The all-cause mortality rates were 7.4% and 4.7% (P = 0.042), composite ischemic event rates were 2.5% and 4.7% (P = 0.056), and bleeding event rates were 2.7% and 4.7% (P = 0.056) in the shortand long-term groups, respectively. In the matched cohort, the incidence of NACE was 9.6% in the short-term DAPT group and 11.6% in the long-term DAPT group, respectively (P = 0.329).The all-cause mortality rates were 6.5% and 4.9% (P = 0.298), composite ischemic event rates were 1.4% and 4.5% (P = 0.009), and bleeding event rates were 2.2% and 4.4% (P = 0.072) in the short- and long-term groups, respectively. Conclusion In patients who successfully underwent transfemoral TAVR, the short- and longterm DAPT groups exhibited similar one-year NACE rates. However, patients in the long-term DAPT group experienced more bleeding and ischemic events.

Background/Aims: To compare the effects of abatacept and conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) on the progression and development of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). Methods: This multi-center retrospective study included RA patients receiving abatacept or csDMARDs who underwent at least two pulmonary function tests and/or chest high-resolution computed tomography (HRCT). We compared the following outcomes between the groups: progression of RA-ILD, development of new ILD in RA patients without ILD at baseline, 28-joint Disease Activity Score with the erythrocyte sedimentation rate (DAS28-ESR), and safety. Longitudinal changes were compared between the groups by using a generalized estimating equation. Results: The study included 123 patients who were treated with abatacept (n = 59) or csDMARDs (n = 64). Nineteen (32.2%) and 38 (59.4%) patients treated with abatacept and csDMARDs, respectively, presented with RA-ILD at baseline. Newly developed ILD occurred in one patient receiving triple csDMARDs for 32 months. Among patients with RA-ILD at baseline, ILD progressed in 21.1% of cases treated with abatacept and 34.2% of cases treated with csDMARDs during a median 21-month follow-up. Longitudinal changes in forced vital capacity and diffusing capacity for carbon monoxide were comparable between the two groups. However, the abatacept group showed a more significant decrease in DAS28-ESR and glucocorticoid doses than csDMARDs group during the follow-up. The safety of both regimens was comparable. Conclusions Abatacept and csDMARDs showed comparable effects on the development and stabilization of RA-ILD. Nevertheless, compared to csDMARDs, abatacept demonstrated a significant improvement in disease activity and led to reduced glucocorticoid use.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.