PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

PURPOSE: This study aimed to evaluate the accuracy of static computer-assisted implant surgery (s-CAIS) by comparing the planned and actual positions of implants placed in a partially edentulous model using surgical templates with different sleeve designs. MATERIALS AND METHODS: Forty-nine mandibular partially edentulous models were scanned using a model scanner to create standard tessellation language (STL) files. The models were divided into seven groups based on sleeve design: Group 1–4 (Open non-metal sleeves with varying heights (3 mm, 6 mm) and buccal widths (5 mm, 6 mm)), Group 5–6 (Closed non-metal sleeves with heights of 3 mm and 6 mm), and Group 7 (Closed metal sleeve). After implant placement at #45 and #47, STL files were generated and analyzed using 3D measurement software. Deviations were measured in terms of horizontal deviation (coronal and apical), vertical deviation, and angular deviation. Statistical significance was evaluated using the Wilcoxon rank sum test, Kruskal-Wallis test, and multiple generalized linear models. RESULTS: While height differences showed no significant deviations (P > .05), there was a tendency for larger apical deviation with increased sleeve height (P < .1). Closed metal and closed non-metal sleeves showed smaller deviations compared to open non-metal sleeves, except in vertical deviation. CONCLUSION Closed sleeves demonstrated greater accuracy in s-CAIS compared to open sleeves. Higher sleeves may improve implant placement accuracy, but buccal insertion width did not significantly affect accuracy.

Background: Transfusion reactions have been under-reported, and the laboratory tests to evaluate the causes of the reactions are unfamiliar to physicians other than transfusion specialists. The paper-based transfusion reaction reporting system previously used in our hospital was one-way, with physicians submitting it to the Department of Transfusion Management. To address this, we developed an electronic reporting system that improves communication with physicians to identify the cause of transfusion reactions and recommend appropriate blood components. Methods: To assess the status of transfusion reaction reporting, transfusion reaction reports and transfusion nursing records of 5 years from 2019 to 2023 were analyzed. The transfusion reaction reporting system comprises two parts: the physician's report and the response from the Department of Transfusion Management. If physicians order blood products for patients with a history of prior transfusion reactions, a pop-up alert appears, warning them to check the details of the previous report. Results: From 2019 to 2023, 2.5% cases of transfusion-related symptoms occurred annually and only 2.6% of transfusion reactions were reported. In 21 out of the 31 cases, the cause was difficult to determine due to inadequate laboratory tests.The attending physicians of 12 cases were given a recommendation to use blood products or to conduct further laboratory tests by the Department of Transfusion Management to reduce recurrence, but the advice was followed only in 4 cases. Conclusion The electronic transfusion reaction reporting system could help physicians conduct appropriate investigations for transfusion reactions and inform physicians regarding the laboratory tests required to be undertaken. It is expected to enhance blood transfusion safety and management by improving communication with physicians.

Background: Transfusion reactions have been under-reported, and the laboratory tests to evaluate the causes of the reactions are unfamiliar to physicians other than transfusion specialists. The paper-based transfusion reaction reporting system previously used in our hospital was one-way, with physicians submitting it to the Department of Transfusion Management. To address this, we developed an electronic reporting system that improves communication with physicians to identify the cause of transfusion reactions and recommend appropriate blood components. Methods: To assess the status of transfusion reaction reporting, transfusion reaction reports and transfusion nursing records of 5 years from 2019 to 2023 were analyzed. The transfusion reaction reporting system comprises two parts: the physician's report and the response from the Department of Transfusion Management. If physicians order blood products for patients with a history of prior transfusion reactions, a pop-up alert appears, warning them to check the details of the previous report. Results: From 2019 to 2023, 2.5% cases of transfusion-related symptoms occurred annually and only 2.6% of transfusion reactions were reported. In 21 out of the 31 cases, the cause was difficult to determine due to inadequate laboratory tests.The attending physicians of 12 cases were given a recommendation to use blood products or to conduct further laboratory tests by the Department of Transfusion Management to reduce recurrence, but the advice was followed only in 4 cases. Conclusion The electronic transfusion reaction reporting system could help physicians conduct appropriate investigations for transfusion reactions and inform physicians regarding the laboratory tests required to be undertaken. It is expected to enhance blood transfusion safety and management by improving communication with physicians.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Class II malocclusions present with a different occlusal pattern to patients with Class I malocclusions, characterized by a large overjet and overbite in the anterior region, resulting in habitual mandibular protrusion during speech or mastication. When rehabilitating Class II patients, it is important to consider lateral and anterior guidance, to ensure balanced posterior guidance during protrusive movements before anterior contact, and to establish freedom in the intercuspal position. In this case, digital tools were used for the full-mouth rehabilitation of a skeletal class II patient. A virtual patient was created using facial scan data, digital facebow transfer and jaw motion tracking. Provisional restorations were fabricated based on the virtual patient. After identifying occlusal interference during anterior movement with the first provisional, the virtual patient was updated and the occlusal design was refined. For the final restorations, the virtual patient was updated again to reflect the functionally and esthetically satisfactory provisional restorations and their adapted occlusion. This digital approach facilitated accurate replication of the dynamic occlusal relationships, resulting in functionally and esthetically successful outcomes.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.