Purpose: This study investigated the rate and factors of spontaneous reduction (SR) during plain radiography in pediatric patients with radial head subluxation (RHS). Methods: We retrospectively reviewed patients aged 5 years or younger with RHS who visited a tertiary hospital emergency department (ED) from March 2015 through February 2022. They were classified into the SR during X-ray, manual reduction (MR) after X-ray, and MR-first groups. We compared the clinical variables, such as ED length of stay, among the 3 groups, and investigated factors associated with SR during radiography. Results: Among a total of 550 enrolled patients, 153 (27.8 %), 177 (32.2%), and 220 (40.0%) belonged to the SR during X-ray, MR after X-ray, and MR-first groups, respectively. Among the groups, no difference was found in the proportions of indoor injury (SR during X-ray, 92.6% vs. MR after X-ray, 79.0% vs. MR-first, 89.0%; P = 0.066). The mean ED length of stay was shorter in the MR-first group than in the equivalent values of the other groups (23.1 minutes vs. 49.0-53.0 minutes; P < 0.001), without differences in the other time intervals. The sole factor associated with SR during radiography was the indoor injury (odds ratio, 3.32; 95% confidence interval, 1.01-10.88; P = 0.048). Conclusion Based on the results of this study, emergency physicians or pediatricians might consider obtaining radiographs first in patients with RHS injured indoors, which can cause SR and exclusion of complications such as iatrogenic injury.

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)

Purpose: This study investigated the rate and factors of spontaneous reduction (SR) during plain radiography in pediatric patients with radial head subluxation (RHS). Methods: We retrospectively reviewed patients aged 5 years or younger with RHS who visited a tertiary hospital emergency department (ED) from March 2015 through February 2022. They were classified into the SR during X-ray, manual reduction (MR) after X-ray, and MR-first groups. We compared the clinical variables, such as ED length of stay, among the 3 groups, and investigated factors associated with SR during radiography. Results: Among a total of 550 enrolled patients, 153 (27.8 %), 177 (32.2%), and 220 (40.0%) belonged to the SR during X-ray, MR after X-ray, and MR-first groups, respectively. Among the groups, no difference was found in the proportions of indoor injury (SR during X-ray, 92.6% vs. MR after X-ray, 79.0% vs. MR-first, 89.0%; P = 0.066). The mean ED length of stay was shorter in the MR-first group than in the equivalent values of the other groups (23.1 minutes vs. 49.0-53.0 minutes; P < 0.001), without differences in the other time intervals. The sole factor associated with SR during radiography was the indoor injury (odds ratio, 3.32; 95% confidence interval, 1.01-10.88; P = 0.048). Conclusion Based on the results of this study, emergency physicians or pediatricians might consider obtaining radiographs first in patients with RHS injured indoors, which can cause SR and exclusion of complications such as iatrogenic injury.

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)

Purpose: This study investigated the rate and factors of spontaneous reduction (SR) during plain radiography in pediatric patients with radial head subluxation (RHS). Methods: We retrospectively reviewed patients aged 5 years or younger with RHS who visited a tertiary hospital emergency department (ED) from March 2015 through February 2022. They were classified into the SR during X-ray, manual reduction (MR) after X-ray, and MR-first groups. We compared the clinical variables, such as ED length of stay, among the 3 groups, and investigated factors associated with SR during radiography. Results: Among a total of 550 enrolled patients, 153 (27.8 %), 177 (32.2%), and 220 (40.0%) belonged to the SR during X-ray, MR after X-ray, and MR-first groups, respectively. Among the groups, no difference was found in the proportions of indoor injury (SR during X-ray, 92.6% vs. MR after X-ray, 79.0% vs. MR-first, 89.0%; P = 0.066). The mean ED length of stay was shorter in the MR-first group than in the equivalent values of the other groups (23.1 minutes vs. 49.0-53.0 minutes; P < 0.001), without differences in the other time intervals. The sole factor associated with SR during radiography was the indoor injury (odds ratio, 3.32; 95% confidence interval, 1.01-10.88; P = 0.048). Conclusion Based on the results of this study, emergency physicians or pediatricians might consider obtaining radiographs first in patients with RHS injured indoors, which can cause SR and exclusion of complications such as iatrogenic injury.

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)

Intractable bleeding from locally advanced breast carcinoma is a rare but challenging clinical problem. Given the patients’ poor overall condition and palliative care status, management options are often limited. Transcatheter arterial embolization (TAE) emerges as a potential alternative to traditional surgical or radiation-based approaches for hemorrhage control. This case report presents a successful application of TAE in managing spontaneous bleeding from a locally advanced breast cancer.