• Annals of Hepato-Biliary-Pancreatic Surgery
• /
• v.28 no.2
• /
• pp.248-261
• /
• 2024

Backgrounds/Aims: This study aimed to investigate patterns and factors affecting recurrence after curative resection for pancreatic ductal adenocarcinoma (PDAC). Methods: Consecutive patients who underwent curative resection for PDAC (2011-21) and consented to data and tissue collection (Barts Pancreas Tissue Bank) were followed up until May 2023. Clinico-pathological variables were analysed using Cox proportional hazards model. Results: Of 91 people (42 males [46%]; median age, 71 years [range, 43-86 years]) with a median follow-up of 51 months (95% confidence intervals [CIs], 40-61 months), the recurrence rate was 72.5% (n = 66; 12 loco-regional alone, 11 liver alone, 5 lung alone, 3 peritoneal alone, 29 simultaneous loco-regional and distant metastases, and 6 multi-focal distant metastases at first recurrence diagnosis). The median time to recurrence was 8.5 months (95% CI, 6.6-10.5 months). Median survival after recurrence was 5.8 months (95% CI, 4.2-7.3 months). Stratification by recurrence location revealed significant differences in time to recurrence between loco-regional only recurrence (median, 13.6 months; 95% CI, 11.7-15.5 months) and simultaneous loco-regional with distant recurrence (median, 7.5 months; 95% CI, 4.6-10.4 months; p = 0.02, pairwise log-rank test). Significant predictors for recurrence were systemic inflammation index (SII) ≥ 500 (hazard ratio [HR], 4.5; 95% CI, 1.4-14.3), lymph node ratio ≥ 0.33 (HR, 2.8; 95% CI, 1.4-5.8), and adjuvant chemotherapy (HR, 0.4; 95% CI, 0.2-0.7). Conclusions: Timing to loco-regional only recurrence was significantly longer than simultaneous loco-regional with distant recurrence. Significant predictors for recurrence were SII, lymph node ration, and adjuvant chemotherapy.

• Journal of Cerebrovascular and Endovascular Neurosurgery
• /
• v.25 no.2
• /
• pp.132-142
• /
• 2023

Objective: Dual antiplatelet therapy (DAPT) is usually temporarily used after stent-assisted coil embolization (SACE), and is commonly converted to mono antiplatelet therapy (MAPT) for indefinitely. In this study, we aimed to find the possibility of discontinuing MAPT, and to determine the proper period of DAPT use. Methods: We used the Standard Sample Cohort DB dataset from the National Health Insurance Sharing Service. Among approximately 1 million people in the dataset, SACE was performed in 214 patients whose data this study analyzed. The relationship between discontinuation of antiplatelet therapy and intracranial hemorrhage or cerebral infarction was analyzed using multiple logistic regression, considering all confounding variables. The survival rate according to the continuation of antiplatelet therapy was obtained using Kaplan-Meier analysis, and the difference in survival rate according to the continuation of antiplatelet therapy was verified using the log-rank test. The hazard ratio according to continuation of antiplatelet therapy was obtained using the Cox proportional hazards model. The analysis was conducted by applying the same statistical method to the duration of DAPT use. Results: Among 214 patients who underwent SACE, 50, 159 and five patients continued, discontinued and did not use antiplatelet therapy (except at the time of procedure), respectively. In multiple logistic regression analysis, discontinuation of antiplatelet agents (including aspirin) and the period of DAPT use did not affect the occurrence of intracranial hemorrhage or cerebral infarction, considering various confounding factors. In the survival analysis according to the continuation of antiplatelet agents, patients who continued to use antiplatelet agents had a higher survival rate than those in other groups (p=0.00). The survival rate was higher in the rest of the group than in the group that received DAPT for three months (p=0.00). Conclusions: Continuation of antiplatelet agents or the period of DAPT use did not affect the occurrence of intracranial hemorrhage or cerebral infarction. Considering the survival rate, it would be better to maintain at least three months of antiplatelet therapy and it might be recommended to continue DAPT use for 12 months.

• The Journal of Korean Academy of Prosthodontics
• /
• v.57 no.4
• /
• pp.342-349
• /
• 2019

Purpose: The purpose of this study was to retrospectively investigate the survival and success rate of implant-supported fixed prosthesis according to the materials in the posterior area. Other purposes were to observe the complications and evaluate the factors affecting failure. Materials and methods: Patients who had been restored implant prosthesis in the posterior area by the same prosthodontist in the department of prosthodontics, dental hospital, Chonbuk National University, in the period from January 2011 to June 2018 were selected for the study. The patient's sex, age, material, location, type of prosthesis and complications were examined using medical records. The KaplanMeier method was used to analyze the survival and success rate. The Log-rank test was conducted to compare the differences between the groups. Cox proportional hazards model was used to assess the association between potential risk factors and success rate. Results: A total of 364 implants were observed in 245 patients, with an average follow-up of 17.1 months. A total of 5 implant prostheses failed and were removed, and the 3 and 5 year cumulative survival rate of all implant prostheses were 97.5 and 91.0, respectively. The 3 and 5 year cumulative success rate of all implant prostheses were 61.1% and 32.9%, respectively. Material, sex, age, location and type of prosthesis did not affect success rate (P>.05). Complications occurred in the order of proximal contact loss (53 cases), retention loss (17 cases), peri-implant mucositis (12 cases), infraocclusion (4 cases) and so on. Conclusion: Considering a high cumulative survival rate of implant-supported fixed prostheses, regardless of the materials, implant restored in posterior area can be considered as a reliable treatment to tooth replacement. However, regular inspections and, if necessary, repairs and adjustments are very important because of the frequent occurrence of complications.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.105-129
• /
• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Distribution Research
• /
• v.5 no.2
• /
• pp.49-68
• /
• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.

• Journal of Korea Port Economic Association
• /
• v.25 no.3
• /
• pp.139-164
• /
• 2009

The purpose of this paper is to investigate the value chain efficiency of Korean port investment by using the newly developed multi-year and multi-stage value chain efficiency model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 14 years(1994-2007) for 20 Korean seaports by using two kinds of DEA models. Empirical main results are as follows: First, Model 1 shows that the ranking order of multi-stage value chain efficiency is Stage 2, Stage 3-1, Stage 1, and Stage 3-2. And according to the value chain average efficiency scores, ranking order is stages 2, 1, 3-1, and 3-2. In Model 2, 3(Incheon, Mogpo, and Jeju) out of 9 ports show the ranking order of Stages 2, 3-2, 3-1, and 1. And value chain average efficiency scores rank in order of Stages 2, 3-2, 3-1, and 1. Second, the difference among the value chain efficiency scores of each stage comes from the efficiency deterioration of all ports except Stages 2 and 1 in Model 1. In Model 2, value chain efficiency scores among the Stages 3-1, 3-2 compared to Stage 1 were deteriorated. The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the multi-year, multi-stage value chain efficiency method for deciding the port investment amount and evaluating the effect of port investment after considering the empirical results of this paper carefully.

• Journal of muscle and joint health
• /
• v.5 no.2
• /
• pp.174-190
• /
• 1998

Osteoporosis has been known as a common complication of rheumatoid arthritis and a major preventable health problem. Lots of studios have demonstrated that changes in life style can help delay or prevent osteoporosis. Therefore nursing intervention related osteoporosis prevention have consisted of education programs aimed at changing dietary and exercise habit. However knowledge gained from education haven't always leaded to behavior change. Therefore it is important to consider other psychological variables in effecting behavior change. Numerous research have found self efficacy and health belief to be an important factor in individual decision making behavior. The purpose of the study was to develop health belief and efficacy expectation promoting program based on Health Belief Model & Self Efficacy Model and to investigate its effects in women with rheumatoid arthritis. For this purpose, one group pretest-post design was used. The subject of the study were 16 women with rheumatoid arthritis in Pusan city and data collection was carried out from April, 1997 to May, 1998. The intervention program was consisted of educating on osteoporosis and enhancing and reinforcing self efficacy by verbal persuasion during the period of 4 weeks. The instruments were used to collect data in this study were Osteoporosis Health Belief Scale, Osteoporosis Self Efficacy Scale, and Osteoporosis Preventive Behavior Scale. Data was analyzed by Wilcoxon signed rank test using SPSS $PC^+$ program. The results are as follows : 1) The behavior should be increased after intervention was supported(Z=-3.5162, p=.0004, diet : Z=-3.2942, p=.0010, exercise). 2) The sub-hypothesis that perceived sensitivity should be increased after intervention was supported (Z=-2.3854, p=.0171). 3) The sub-hypothesis that perceived severity should be increased after intervention was rejected(Z=-1.4327, p=.1520). 4) The sub-hypothesis that perceived benefit should be increased after intervention was supported(Z=-2.6410, p=.0083). 5) The sub-hypothesis that perceived barrier should be decreased after intervention was supported (Z=-2.4138, p=.0158). 6) The sub-hypothesis that efficacy expectation should be increased after intervention was supported(Z=-3.5162, p=.0004). As a conclusion, it was found that health belief and self efficacy promoting program was an effective nursing intervention for preventing osteoporosis of rheumatoid arthritis.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.6 no.2
• /
• pp.45-56
• /
• 2007

As function and scale of the transit center get larger, the efficient guidance system in the transit center is essential for transit users in order to find their efficient routes. Although there are several studies concerning optimal path for the road, but insufficient studies are executed about optimal path inside the building. Thus, this study is to develop the algorithm about optimal path for car owner from the basement parking lot to user's destination in the transfer station. Based on Dijkstra algorithm which calculate horizontal distance, several factors such as fatigue, freshness, preference, and required time in using moving devices are objectively computed through rank-sum and arithmetic-sum method. Moreover, optimal public transportation is provided for transferrer in the transfer station by Neuro-Fuzzy model which is reflected on people's tendency about public transportation mode choice. Lastly, some scenarios demonstrate the efficiency of optimal path algorithm for pedestrian in this study. As a result of verification the case through the model developed in this study is 75 % more effective in the scenario reflected on different vertical distance, and $24.5\;{\sim}\;107.7\;%$ more effective in the scenario considering different horizontal distance, respectively.

• Journal of Gastric Cancer
• /
• v.5 no.2
• /
• pp.113-119
• /
• 2005

Purpose: Some controversies exist over the prognostic values of lymphatic, venous, and neural invasion in patients with gastric cancer. This study was conducted to confirm the prognostic values of these histopathologic factors in gastric cancer patients who received a gastrectomy. Materials and Methods: Data for clinicopathologic factors and clinical outcomes were collected retrospectively from the medical records of 1,018 gastric cancer patients who received a gastrectomy at Yeungnam University Medical Center between January 1995 and December 1999. A statistical analysis was done using the SPSS program for Windows (Version 10.0, SPSS Inc., USA). The Kaplan-Meier method was used for the survival analysis. Prognostic factors were analyzed by using a multivariate analysis with Cox proportional hazard regression model. Results: Ages ranged from 21 to 79 (median age, 56). A univariate analysis revealed that age, tumor size, location, gross type, depth of invasion, extent of gastrectomy or lymph node dissection, lymph node metastasis, distant metastasis, lymphatic invasion, venous invasion, neural invasion, pathologic stage, histologic type, and curability of surgery had statistical significance. Among these factors, lymph node metastasis, curability of surgery, neural invasion, lymphatic invasion, and depth of invasion were found to be independent prognostic factors by using a multivariate analysis. Venous invasion showed no prognostic value in the multivariate analysis. Conclusion: Neural invasion and lymphatic invasion are useful parameters in determining a prognosis for gastric cancer patients.

• Journal of Preventive Medicine and Public Health
• /
• v.38 no.2
• /
• pp.208-214
• /
• 2005

Objectives: This study was performed to examine the factors affecting re-smoking in male workers. Methods: A self-administrated questionnaire survey was conducted during April 2003 to examine the smoking state of 1,154 employees of a company that launched a smoking cessation campaign in1998. Five hundred and eighty seven persons, who had stopped smoking for at least one week, were selected as the final study subjects. This study collected data on smoking cessation success or failure for 6 months, and looked at the factors having an effect on re-smoking within this period. This study employed the Health Belief Model as its theoretical basis. Results: The re-smoking rate of the 587 study subjects who had stopped smoking for at least one week was 44.8% within the 6 month period. In a simple analysis, the re-smoking rates were higher in workers with a low age, on day and night shifts, blue collar, of a low rank, where this was their second attempt at smoking cessation and for those with a shorter job duration (p<0.05). Of the cues to action variables in the Heath Belief Model, re-smoking was significantly related with the perceived susceptibility factor, economic advantages of smoking cessation among the perceived benefits factor, the degree of cessation trial's barrier of the perceived barriers factor, smoking symptom experience, recognition of the degree of harmfulness of environmental tobacco smoke and the existence of chronic disease due to smoking (p<0.05). In the multiple logistic regression analysis for re-smoking, the significant variables were age, perceived susceptibility for disease, economic advantages due to smoking cessation, the perceived barrier for smoking cessation, recognition on the degree of harmfulness of environmental tobacco smoke, the existence of chronic disease due to smoking and the number of attempts at smoking cessation (p<0.05). Conclusion: From the result of this study, for an effective smoking ban policy within the work place, health education that improves the knowledge of the adverse health effects of smoking and the harmfulness of environmental tobacco smoke will be required, as well as counter plans to reduce the barriers for smoking cessation.