Objectives: To investigate the causal factors of proximal contact loss (PCL) between an implant prosthesis and the adjacent natural teeth using cast model analysis. Materials and Methods: Patients who underwent restoration using dental implants in the posterior region were analyzed. To identify factors associated with PCL incidence, cast model analyses were conducted based on sex, implant site, jaw position, Angle’s classification, anterior overbite, preexisting interproximal gap between consecutive (mesial side) natural teeth adjacent to the implant, generalized gap of the full arch, and mandibular anterior crowding. Chi-square, multivariate logistic regression, and multivariate generalized estimating equation (GEE) analyses were used to evaluate the impact of each factor. The incidence of PCL over time was analyzed using Kaplan–Meier analysis. Results: Of 653 implants, 293 implants were selected from 240 patients. Ninety implant sites (30.7%) showed PCL between the implant prostheses and the adjacent teeth. The analysis of PCL incidence revealed a gradual increase over time, with half of PCL cases occurring by 19.2 months. The chisquare test revealed significant associations between a pre-existing interproximal gap between adjacent natural teeth and a generalized overall gap in the corresponding arch and PCL (P=0.002 and P=0.027). The logistic regression (P=0.007, odds ratio [OR]: 2.684) and GEE (P=0.003, OR: 3.255) showed significant correlations between PCL and a pre-existing interproximal gap between adjacent natural teeth. Conclusion The occurrence of PCL between implant prostheses and adjacent teeth is influenced by the pre-existing interproximal gap between consecutive natural teeth adjacent to the implant. This factor should be carefully monitored.

Objectives: To evaluate the factors that influence the survival of dental implants and marginal bone loss (MBL) in patients taking osteoporosis or osteopenia medication. Materials and Methods: This study included patients who underwent dental implant treatment after taking medication for osteoporosis or osteopenia. Electronic medical records were used to collect data of patient age, sex, age at osteoporosis or osteopenia diagnosis, types of medications, age at medication initiation, duration of medication before implant surgery, whether the medication was paused before surgery, paused duration of medication, implant survival status, and MBL before and after prosthetic treatment. Firth’s logistic regression was used to analyze the relationships between each variable and implant survival as well as between MBL before and after prosthetic treatment. Results: Of the 267 patients, 111 with 209 implants were included in the study. The mean observation period was 57.9 months. The survival rate was 92.8% at the patient level and 96.2% at the implant level. No significant associations were found between implant survival and any of the variablesexamined. MBL before prosthetic treatment was significantly associated with use of receptor activator of nuclear factor-κB ligand (RANKL) inhibitors(P=0.032) and bone formation stimulators (P=0.022). Comparing the concurrent and single use of bisphosphonates and RANKL inhibitors, only the use of RANKL inhibitors alone was significantly associated with MBL before prosthetic treatment (P=0.039). MBL after prosthetic treatment was significantly associated with injection method among the routes of drug administration (P=0.011). Conclusion The implant survival rate in patients undergoing medical treatment for osteoporosis or osteopenia was comparable to the general implant survival rate. MBL before prosthetic treatment was associated with type of anti-osteoporotic medication, whereas MBL after prosthetic treatment was correlated with drug administration route. Further studies with larger sample sizes are required.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Objectives: To investigate the causal factors of proximal contact loss (PCL) between an implant prosthesis and the adjacent natural teeth using cast model analysis. Materials and Methods: Patients who underwent restoration using dental implants in the posterior region were analyzed. To identify factors associated with PCL incidence, cast model analyses were conducted based on sex, implant site, jaw position, Angle’s classification, anterior overbite, preexisting interproximal gap between consecutive (mesial side) natural teeth adjacent to the implant, generalized gap of the full arch, and mandibular anterior crowding. Chi-square, multivariate logistic regression, and multivariate generalized estimating equation (GEE) analyses were used to evaluate the impact of each factor. The incidence of PCL over time was analyzed using Kaplan–Meier analysis. Results: Of 653 implants, 293 implants were selected from 240 patients. Ninety implant sites (30.7%) showed PCL between the implant prostheses and the adjacent teeth. The analysis of PCL incidence revealed a gradual increase over time, with half of PCL cases occurring by 19.2 months. The chisquare test revealed significant associations between a pre-existing interproximal gap between adjacent natural teeth and a generalized overall gap in the corresponding arch and PCL (P=0.002 and P=0.027). The logistic regression (P=0.007, odds ratio [OR]: 2.684) and GEE (P=0.003, OR: 3.255) showed significant correlations between PCL and a pre-existing interproximal gap between adjacent natural teeth. Conclusion The occurrence of PCL between implant prostheses and adjacent teeth is influenced by the pre-existing interproximal gap between consecutive natural teeth adjacent to the implant. This factor should be carefully monitored.

Objectives: To evaluate the factors that influence the survival of dental implants and marginal bone loss (MBL) in patients taking osteoporosis or osteopenia medication. Materials and Methods: This study included patients who underwent dental implant treatment after taking medication for osteoporosis or osteopenia. Electronic medical records were used to collect data of patient age, sex, age at osteoporosis or osteopenia diagnosis, types of medications, age at medication initiation, duration of medication before implant surgery, whether the medication was paused before surgery, paused duration of medication, implant survival status, and MBL before and after prosthetic treatment. Firth’s logistic regression was used to analyze the relationships between each variable and implant survival as well as between MBL before and after prosthetic treatment. Results: Of the 267 patients, 111 with 209 implants were included in the study. The mean observation period was 57.9 months. The survival rate was 92.8% at the patient level and 96.2% at the implant level. No significant associations were found between implant survival and any of the variablesexamined. MBL before prosthetic treatment was significantly associated with use of receptor activator of nuclear factor-κB ligand (RANKL) inhibitors(P=0.032) and bone formation stimulators (P=0.022). Comparing the concurrent and single use of bisphosphonates and RANKL inhibitors, only the use of RANKL inhibitors alone was significantly associated with MBL before prosthetic treatment (P=0.039). MBL after prosthetic treatment was significantly associated with injection method among the routes of drug administration (P=0.011). Conclusion The implant survival rate in patients undergoing medical treatment for osteoporosis or osteopenia was comparable to the general implant survival rate. MBL before prosthetic treatment was associated with type of anti-osteoporotic medication, whereas MBL after prosthetic treatment was correlated with drug administration route. Further studies with larger sample sizes are required.