• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1675-1682
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• 2012

Aim: The study aim was to determine the frequency with which women decline clinicians' treatment recommendations and variations in this frequency by age, cancer and service descriptors. Design: The study included 36,775 women diagnosed with early invasive breast cancer in 1998-2005 and attending Australian and New Zealand breast surgeons. Rate ratios for declining treatment were examined by descriptor, using bilateral and multiple logistic regression analyses. Proportional hazards regression was used in exploratory analyses of associations with breast cancer death. Results: 3.4% of women declined a recommended treatment of some type, ranging from 2.6% for women under 40 years to 5.8% for those aged 80 years or more, and with parallel increases by age presenting for declining radiotherapy (p<0.001) and axillary surgery (p=0.006). Multiple regression confirmed that common predictors of declining various treatments included low surgeon case load, treatment outside major city centres, and older age. Histological features suggesting a favourable prognosis were often predictive of declining various treatments, although reverse findings also applied with women with positive nodal status being more likely to decline a mastectomy and those with larger tumours more likely to decline chemotherapy. While survival analyses lacked statistical power due to small numbers, higher risks of breast cancer death were suggested, after adjusting for age and conventional clinical risk factors, (1) for women not receiving breast surgery for unstated reasons (RR=2.29; p<0.001); and (2) although not approaching statistical significance $p{\geq}0.200$), for women declining radiotherapy (RR=1.22), a systemic therapy (RR1.11), and more specifically, chemotherapy (RR=1.41). Conclusions: Women have the right to choose their treatments but reasons for declining recommendations require further study to ensure that choices are well informed and clinical outcomes are optimized.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.191-197
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• 2017

The purpose of this study was to identify the predictive power of the effect of pre-early childhood teachers' self-efficacy and ego resiliency on the SNS addiction tendency. To this end, a self - administered survey was conducted for 117 students in the first, second, and third grades of junior college of junior college in metropolitan area and multiple regression analysis was performed based on the collected data. The research findings showed that self-efficacy and ego resiliency had a negative effect on SNS addiction tendency. This suggests that self-efficacy and ego resiliency are statistically significant variables in predicting pre-early childhood teachers' SNS addiction tendency. These results also suggest that specific measures for enhancing self-efficacy and ego resiliency should be considered as precautionary measures to prevent SNS addiction of pre-early childhood teachers.

• Korean Journal of Childcare and Education
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• v.6 no.1
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• pp.67-93
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• 2010

The purpose of the present study was to examine how 25~36-month-old toddlers' temperament, mother's rearing attitude, teacher interaction relates to their language abilities and to analyze the predictive power of these variables in predicting language ability. The subjects were 206 toddlers, 206 mothers and 38 class teachers at child care center in G metropolitan city. The collected data were analyzed using Pearson's correlation analysis and stepwise multiple regression analysis on SPSS program. The results of this study were as follow. First, there was a significant relationship between mother's rearing attitude, teacher interaction and toddlers language abilities. However, toddlers' temperament showed no significant correlations with toddlers language abilities. Second, teacher's positive attitude was the best predictor of toddlers language abilities. Toddlers' reactivity and mother's rational guidance were also predictors of toddlers' language abilities.

• Journal of Trauma and Injury
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• v.19 no.1
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• pp.35-40
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• 2006

Purpose: Injuries are the third leading cause of death in Korea. Isolated chest injury is not uncommon and shows high mortality and morbidity. Several scoring systems are used for triage and stratification for trauma patients, but no standard system is accepted. We aimed to analyze the accuracy of identification of isolated chest injury by using several scoring systems. Methods: We reviewed a total of 75 patients admitted with isolated chest injury between January 2005 and October 2005. Medical records were reviewed by using the Injury Severity Score (ISS), the Revised Trauma Score (RTS), and the Trauma and Injury Severity Score (TRISS). The scoring systems were compared by using statistics methods. Results: The overall predictive accuracy of the TRISS was 12.5%, 12.0% greater than those of the RTS and the ISS. By using the area under the receiver operating characteristic (AUROC) curve, the TRISS showed an excellent discriminative power (AUROC 0.931) compared to the ISS (AUROC 0.926) and the RTS (AUROC 0.872). Conclusion: Compared with the RTS and the ISS, the TRISS is an easily applied tool with excellent prognostic abilities for isolated chest trauma patients. However, the TRISS, the ISS, and the RTS showed high specificity and low sensitivity, so another scoring system is required for triage and stratification of isolated chest injury patients.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.79-87
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• 2011

A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

• Korean Journal of Adult Nursing
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• v.17 no.3
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• pp.464-473
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• 2005

Purpose: The purpose of this study was to evaluate and compare the predictive ability of three mortality scoring systems; Acute Physiology and Chronic Health Evaluation(APACHE) III, Simplified Acute Physiology Score(SAPS) II, and Mortality Probability Model(MPM) II in discriminating in-hospital mortality for intensive care unit(ICU) patients with spontaneous intracerebral hemorrhage. Methods: Eighty-nine patients admitted to the ICU at a university hospital in Daejeon Korea were recruited for this study. Medical records of the subject were reviewed by a researcher from January 1, 2003 to March 31, 2004, retrospectively. Data were analyzed using SAS 8.1. General characteristic of the subjects were analyzed for frequency and percentage. Results: The results of this study were summarized as follows. The values of the Hosmer-Lemeshow's goodness-of-fit test for the APACHE III, the SAPS II and the MPM II were chi-square H=4.3849 p=0.7345, chi-square H=15.4491 p=0.0307, and chi-square H=0.3356 p=0.8455, respectively. Thus, The calibration of the MPM II found to be the best scoring system, followed by APACHE III. For ROC curve analysis, the areas under the curves of APACHE III, SAPS II, and MPM II were 0.934, 0.918 and 0.813, respectively. Thus, the discrimination of three scoring systems were satisfactory. For two-by-two decision matrices with a decision criterion of 0.5, the correct classification of three scoring systems were good. Conclusion: Both the APACHE III and the MPM II had an excellent power of mortality prediction and discrimination for spontaneous intracerebral hemorrhage patients in ICU.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.9-20
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• 2016

Ozone has been a problem in big cities. That is secondary air pollutant produced by nitrogen oxide and VOCs in the atmosphere. In order to solve this, the first to be the analysis of the $NO_x$ and VOCs. The main source of nitrogen oxide is the road mobile. Industrial sources in Seoul are particularly low, and mobile traffics on roads are large, so 45% of total $NO_x$ are estimated that road mobile emissions in Seoul. Thus, it is necessary to clarify the relation with the activity of road mobile source and $NO_x$ concentration. In this study, we analyzed the 4 locations with roadside automatic monitoring systems in their center. The V.K.T. calculating areas are set in circles with 50 meter spacing, 50 meter to 500 meter from their center. We assumed the total V.K.T. in the set radius affect the $NO_x$ concentration in the center. We used the hourly $NO_x$ concentrations data for the 4 observation points in July for the interference of the other sources are minimized. We used the intersection traffic survey data of all direction for construction of the V.K.T. data, the mobile activities on the roads. ArcGIS application was used for calculating the length of roads in the set radius. The V.K.T. data are multiplied by segment traffic volume and length of roads. As a result, the $NO_x$ concentration can be expressed as linear function formula for V.K.T. with high predictive power. Moreover we separated background concentration and concentrations due to road mobile source. These results can be used for forecasting the effect of traffic demand management plan.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.93-99
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• 2017

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

• Toxicological Research
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• v.32 no.1
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• pp.47-56
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• 2016

The identification of biomarkers for the early detection of acute kidney injury (AKI) is clinically important. Acute kidney injury (AKI) in critically ill patients is closely associated with increased morbidity and mortality. Conventional biomarkers, such as serum creatinine (SCr) and blood urea nitrogen (BUN), are frequently used to diagnose AKI. However, these biomarkers increase only after significant structural damage has occurred. Recent efforts have focused on identification and validation of new noninvasive biomarkers for the early detection of AKI, prior to extensive structural damage. Furthermore, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Our previous study suggested that pyruvate kinase M2 (PKM2), which is excreted in the urine, is a sensitive biomarker for nephrotoxicity. To appropriately and optimally utilize PKM2 as a biomarker for AKI requires its complete characterization. This review highlights the major studies that have addressed the diagnostic and prognostic predictive power of biomarkers for AKI and assesses the potential usage of PKM2 as an early biomarker for AKI. We summarize the current state of knowledge regarding the role of biomarkers and the molecular and cellular mechanisms of AKI. This review will elucidate the biological basis of specific biomarkers that will contribute to improving the early detection and diagnosis of AKI.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.426-432
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• 2006

This paper describes the concepts of Static Line Rating (SLR) and Dynamic Line Rating (DLR) and the computational methods to demonstrate them. Calculation of the line capacity needs the heat balance equation which is also used for computing the reduced tension in terms of line aging. SLR is calculated with the data from the worst condition of weather throughout the year. Even now, the utilization ratio is obtained from this SLR data in Korea. DLR is the improved method compared to SLR. A process for DLR reveals not only improved line ratings but also more accurate allowed line ratings based on line aging and real time conditions of weather. In order to reflect overhead transmission line aging in DLR, this paper proposes the method that considers the amount of decreased tension since the lines have been installed. Therefore, the continuous allowed temperature for remaining life time is newly acquired. In order to forecast DLR, this paper uses weather forecast models, and applies the concept of Thermal Overload Risk Probability (TORP). Then, the new concept of Dynamic Utilization Ratio (DUR) is defined, replacing Static Utilization Ratio (SUR). For the case study, the two main transmission lines which are responsible for the north bound power flow in the Seoul metropolitan area are chosen for computing line rating and utilization ratio. And then line rating and utilization ratio are analyzed for each transmission line, so that comparison of the present and estimated utilization ratios becomes available. Finally, this paper proves the validity of predictive DUR as the objective index, with simulations of emergency state caused by system outages, overload and so on.