The Korean Workers’ Compensation-National Health Insurance Service (KoWorC-NHIS) cohort was established to investigate the longitudinal health outcomes of Korean workers who have been compensated for occupational injuries or diseases. This cohort study, which utilized data spanning from 2004 to 2015, merged workers’ compensation insurance claim data with the National Health Insurance Database (NHID), encompassing 858,793 participants. The data included socio-demographic factors such as age, sex, income, address, insurance type, and disability grade. It also covered the types of occupational accidents, International Classification of Diseases, 10th revision codes for diseases or accidents, work tenure, industry, occupation code, and company size. Additional details such as the occupational hire date, date of claim, date of recognition, and affected body parts were recorded. The cohort predominantly consisted of male workers (80.0%), with the majority experiencing their first occupational accident in their 40s (27.6%) or 50s (25.3%). Notably, 93.1% of the cases were classified as occupational injuries. By integrating this data with that from the NHID, updates on health utilization, employment status, and income changes were made annually. The follow-up period for this study is set to conclude in 2045.

Purpose: To investigate the influence of violence experience and response of coping with violence on professional QoL among emergency department. Methods: This cross-sectional study, included 179 subjects. Data were collected online from June 24 to July 31, 2022, and were analyzed using independent t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple regression. Results: In the compassion satisfaction category, the problem focused coping (β=.328, p<.001) was a significant influencing factor (adj. R2 =.103) (F=21.36, p<.001). In the burnout category, violence response (β=.460, p<.001), problem focused coping (β=-.306, p<.001), and violence experience (β=.151, p=.030) were significant influencing factors (adj. R2 =.288) (F=24.99, p<.001). In the secondary traumatic stress category, violence response (β=.587, p<.001) and emergency department career (β=.177, p=.011) were significant influencing factors (adj. R2 =.383) (F=41.90, p<.001). Conclusion To improve professional QoL, it is necessary to understand the current situation related to violence and prepare a coping support system and intervention to prevent violence experiences and reduce negative consequences related to violence for a safe working environment for emergency department nurses.

Purpose: To investigate the influence of violence experience and response of coping with violence on professional QoL among emergency department. Methods: This cross-sectional study, included 179 subjects. Data were collected online from June 24 to July 31, 2022, and were analyzed using independent t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple regression. Results: In the compassion satisfaction category, the problem focused coping (β=.328, p<.001) was a significant influencing factor (adj. R2 =.103) (F=21.36, p<.001). In the burnout category, violence response (β=.460, p<.001), problem focused coping (β=-.306, p<.001), and violence experience (β=.151, p=.030) were significant influencing factors (adj. R2 =.288) (F=24.99, p<.001). In the secondary traumatic stress category, violence response (β=.587, p<.001) and emergency department career (β=.177, p=.011) were significant influencing factors (adj. R2 =.383) (F=41.90, p<.001). Conclusion To improve professional QoL, it is necessary to understand the current situation related to violence and prepare a coping support system and intervention to prevent violence experiences and reduce negative consequences related to violence for a safe working environment for emergency department nurses.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

The Korean Workers’ Compensation-National Health Insurance Service (KoWorC-NHIS) cohort was established to investigate the longitudinal health outcomes of Korean workers who have been compensated for occupational injuries or diseases. This cohort study, which utilized data spanning from 2004 to 2015, merged workers’ compensation insurance claim data with the National Health Insurance Database (NHID), encompassing 858,793 participants. The data included socio-demographic factors such as age, sex, income, address, insurance type, and disability grade. It also covered the types of occupational accidents, International Classification of Diseases, 10th revision codes for diseases or accidents, work tenure, industry, occupation code, and company size. Additional details such as the occupational hire date, date of claim, date of recognition, and affected body parts were recorded. The cohort predominantly consisted of male workers (80.0%), with the majority experiencing their first occupational accident in their 40s (27.6%) or 50s (25.3%). Notably, 93.1% of the cases were classified as occupational injuries. By integrating this data with that from the NHID, updates on health utilization, employment status, and income changes were made annually. The follow-up period for this study is set to conclude in 2045.

The Korean Workers’ Compensation-National Health Insurance Service (KoWorC-NHIS) cohort was established to investigate the longitudinal health outcomes of Korean workers who have been compensated for occupational injuries or diseases. This cohort study, which utilized data spanning from 2004 to 2015, merged workers’ compensation insurance claim data with the National Health Insurance Database (NHID), encompassing 858,793 participants. The data included socio-demographic factors such as age, sex, income, address, insurance type, and disability grade. It also covered the types of occupational accidents, International Classification of Diseases, 10th revision codes for diseases or accidents, work tenure, industry, occupation code, and company size. Additional details such as the occupational hire date, date of claim, date of recognition, and affected body parts were recorded. The cohort predominantly consisted of male workers (80.0%), with the majority experiencing their first occupational accident in their 40s (27.6%) or 50s (25.3%). Notably, 93.1% of the cases were classified as occupational injuries. By integrating this data with that from the NHID, updates on health utilization, employment status, and income changes were made annually. The follow-up period for this study is set to conclude in 2045.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Purpose: To investigate the influence of violence experience and response of coping with violence on professional QoL among emergency department. Methods: This cross-sectional study, included 179 subjects. Data were collected online from June 24 to July 31, 2022, and were analyzed using independent t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple regression. Results: In the compassion satisfaction category, the problem focused coping (β=.328, p<.001) was a significant influencing factor (adj. R2 =.103) (F=21.36, p<.001). In the burnout category, violence response (β=.460, p<.001), problem focused coping (β=-.306, p<.001), and violence experience (β=.151, p=.030) were significant influencing factors (adj. R2 =.288) (F=24.99, p<.001). In the secondary traumatic stress category, violence response (β=.587, p<.001) and emergency department career (β=.177, p=.011) were significant influencing factors (adj. R2 =.383) (F=41.90, p<.001). Conclusion To improve professional QoL, it is necessary to understand the current situation related to violence and prepare a coping support system and intervention to prevent violence experiences and reduce negative consequences related to violence for a safe working environment for emergency department nurses.

The Korean Workers’ Compensation-National Health Insurance Service (KoWorC-NHIS) cohort was established to investigate the longitudinal health outcomes of Korean workers who have been compensated for occupational injuries or diseases. This cohort study, which utilized data spanning from 2004 to 2015, merged workers’ compensation insurance claim data with the National Health Insurance Database (NHID), encompassing 858,793 participants. The data included socio-demographic factors such as age, sex, income, address, insurance type, and disability grade. It also covered the types of occupational accidents, International Classification of Diseases, 10th revision codes for diseases or accidents, work tenure, industry, occupation code, and company size. Additional details such as the occupational hire date, date of claim, date of recognition, and affected body parts were recorded. The cohort predominantly consisted of male workers (80.0%), with the majority experiencing their first occupational accident in their 40s (27.6%) or 50s (25.3%). Notably, 93.1% of the cases were classified as occupational injuries. By integrating this data with that from the NHID, updates on health utilization, employment status, and income changes were made annually. The follow-up period for this study is set to conclude in 2045.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.