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: Providing optimal nutrition to patients with acute respiratory failure is difficult because nutritional requirements vary according to disease severity and comorbidities. In 2021, the National Medical Center initiated a protocol for screening upon admission and regular monitoring by a multidisciplinary nutritional support team (NST), for all patients in the medical intensive care unit (ICU). This study aimed to evaluate the effects of routine NST monitoring and active intervention on the clinical outcomes of patients with acute respiratory failure. Methods: Patients with acute respiratory failure requiring high-flow nasal cannula, non-invasive ventilation, or mechanical ventilation were included. The primary outcome was 28-day mortality after ICU admission. Secondary outcomes included the supplied/target calorie ratio, supplied/target protein ratio on day 7, and complications. Results: In total, 152 patients were included in the analysis. The patients were divided into a pre-monitoring (n=96) and post-monitoring groups (n=56). More patients in the post-monitoring group received NST intervention and had earlier initiation of enteral feeding. In survival analysis, 28-day mortality was significantly lower in post-monitoring group (adjusted hazard ratio, 0.42; 95% CI, 0.24–0.74). The ratio of achievement for required calories and protein on day 7 was higher, but not significantly, in the post-monitoring group. No significant differences were observed in the incidence of complications. Conclusions Regular NST monitoring in the ICU could have contributed to a reduced risk of 28-day mortality in critically ill patients with acute respiratory failure.

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.

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.