Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Since the era of evidence-based medicine, it has become a matter of course to use statistics to create objective evidence in clinical research. As an extension of this, it has become essential in clinical research to calculate the correct sample size to demonstrate a clinically significant difference before starting the study.Also, because sample size calculation methods vary from study design to study design, there is no formula for sample size calculation that applies to all designs. It is very important for us to understand this. In this review, each sample size calculation method suitable for various study designs was introduced using the R program (R Foundation for Statistical Computing). In order for clinical researchers to directly utilize it according to future research, we presented practice codes, output results, and interpretation of results for each situation.

PURPOSE: Laparoscopic gastrectomy in early gastric cancer patients is accepted as standard, but it is sometimes challenging for patients who refuse blood transfusions such as Jehovah's Witness (JW) patients, because of the risk of bleeding related to radical lymph node dissection. This study aimed to confirm the adequacy and safety of laparoscopic gastrectomy with D1+ lymphadenectomy in JW patients. METHODS: From January 2009 to December 2015, 265 gastric cancer patients underwent laparoscopic gastrectomy in our institute. Among them, there were 25 JW, and they were statistically matched with 75 patients from the control groups depending on age, sex, and body mass index (BMI). In a retrospective review, patient laboratory values and their pathology results were analysed. RESULTS: There was no significant difference when comparing the clinical characteristics of JW and control groups. There was no statistically significant difference in blood loss or operation time between the two groups. Mean blood loss was 202.4±172.6 ml in the JW group and 179.7±163.8 ml in the control group (p=0.556). The number of retrieved lymph nodes was 27.8±13.9 in the JW group and 29.3±12.1 in the control group (p=0.607). Haemoglobin and haematocrit were measured after surgery and there was no statistically significant difference between the two groups. CONCLUSION: Laparoscopic D1+ gastrectomy in a JW may be performed with an equivalent risk to the control group. Laparoscopic gastrectomy can be applied to Jehovah's Witnesses if the specialied cancer center has sufficient experience in stomach cancer surgery, even if there is not enough experience in bloodless surgery.
Blood Transfusion ; Bloodless Medical and Surgical Procedures ; Body Mass Index ; Case-Control Studies* ; Gastrectomy ; Hemorrhage ; Humans ; Jehovah's Witnesses ; Lymph Node Excision* ; Lymph Nodes* ; Pathology ; Retrospective Studies ; Stomach Neoplasms*

Purpose: This study aimed to compare the effects of oral swab and non-swab using cold water gauze on patients’ thirst and oral status following nasal surgery under general anesthesia. Methods: A non-equivalent control group was applied. Participants were divided into a control group (n=30) that was treated by cared for with cold water gauze application without oral swab and an experimental group (n=29) that was treated cared for with oral swab using cold water gauze after nasal surgery at the G University Hospital in Korea. Data were collected from May 14, 2020, to April 30, 2021, and analyzed using descriptive statistics, a chi-squared test, independent t-test, Wilcoxon signed-ranks test, and Mann-Whitney test. Results: The results showed no significant differences between the two groups in thirst (U=-0.04, p=.693) and overall oral condition (U=-0.34, p=.813) after the intervention. However, participants’ thirst and oral condition were significantly improved in each group after intervention. Conclusions It was confirmed that both oral care methods reduced thirst and improved oral condition after nasal surgery. These findings indicate the need for intervention for patients’ thirst and oral condition after nasal surgery. Furthermore, they show that these oral care protocols can be used as a safe and effective nursing intervention for patients who undergo nasal surgery under general anesthesia.

Purpose: The purpose of this study was to assess the dimensional change of 3D-printed dentures after post-curing. Materials and Methods: The upper and lower dentures were designed in Exocad DentalCAD software and exported as STL files. The upper and lower dentures were printed from 10 STL files using a DLP-type dental 3D printer. The printed upper and lower dentures were cleaned, and a scan file was created using a model scanner before and after post-curing. The dimensional change was evaluated by superimposing the scanned denture files before and after post-curing and measuring the distance between measurement points on the denture. SPSS was used for statistics, and the level of significance was 5%. Results: The maxillary denture reduced in size during post-curing, with the most notable color change occurring in the posterior palatal region. The reduction in anteroposterior maxillary denture length (A-D, A-E, A-F), as well as the distance between the first molars on both sides (B-C), was statistically significant. After post-curing, the mandibular denture showed more noticeable color change in the posteriorly buccal and lingual region. The decrease of length on the posterior (A-M, A-D, A-E, A-L, A-H, A-I, H-I) and lingual (J-K, L-M) sides of the denture were statistically significant. Conclusion There was significant dimensional change in both the length and width of the 3D-printed maxillary and mandibular dentures after post-curing in this experiment. Consequently, it is seemed necessary to develop post-curing techniques and materials that reduce such denture deformation.

Background: The present study employed National Health Insurance Data to explore complex regional pain syndrome (CRPS) updated epidemiology in a Korean context. Methods: A CRPS cohort for the period 2009-2016 was created based on Korean Standard Classification of Diseases codes alongside the national registry. The general CRPS incidence rate and the yearly incidence rate trend for every CRPS type were respectively the primary and secondary outcomes. Among the analyzed risk factors were age, sex, region, and hospital level for the yearly trend of the incidence rate for every CRPS. Statistical analysis was performed via the chi-square test and the linear and logistic linear regression tests. Results: Over the research period, the number of registered patients was 122,210.The general CRPS incidence rate was 15.83 per 100,000, with 19.5 for type 1 and 12.1 for type 2. The condition exhibited a declining trend according to its overall occurrence, particularly in the case of type 2 (P < 0.001). On the other hand, registration was more pervasive among type 1 compared to type 2 patients (61.7% vs.38.3%), while both types affected female individuals to a greater extent. Regarding age, individuals older than 60 years of age were associated with the highest prevalence in both types, regardless of sex (P< 0.001). Conclusions CRPS displayed an overall incidence of 15.83 per 100,000 in Korea and a declining trend for every age group which showed a negative association with the aging shift phenomenon.