• Journal of Distribution Research
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• v.14 no.1
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• pp.45-65
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• 2009

As a number of people using the internet for their shopping steadily rises, it is increasingly important for retailers to understand why consumers decide to buy products via online or offline. The main purpose of this study is to develop and test a model that enhance our understanding of how consumers respond future online and offline channels for their purchasing. Rather than merely adopting statistical models like most other studies in this field, the present study develops a model that combines double-cohort method with multinomial logit model. It is desirable if one can adopt an overall encompassing criterion in the study of consumer behaviors form diverse sales channels. This study uses the concept of cohort or aging to enable this comparison. It enables us to analyze how consumers respond to online and offline channels as people aged by measuring their shopping behavior for an online and offline retailers and their subsequent purchase intentions. Based on some empirical findings, this study concludes with policy implications and some necessary fields of future studies desirable.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.413-421
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• 2020

Objective: A widely applicable, non-invasive screening method for non-alcoholic fatty liver disease (NAFLD) is needed. We aimed to develop and validate an index combining computed tomography (CT) and routine clinical data for screening for NAFLD in a large cohort of adults with pathologically proven NAFLD. Materials and Methods: This retrospective study included 2218 living liver donors who had undergone liver biopsy and CT within a span of 3 days. Donors were randomized 2:1 into development and test cohorts. CTL-S was measured by subtracting splenic attenuation from hepatic attenuation on non-enhanced CT. Multivariable logistic regression analysis of the development cohort was utilized to develop a clinical-CT index predicting pathologically proven NAFLD. The diagnostic performance was evaluated by analyzing the areas under the receiver operating characteristic curve (AUC). The cutoffs for the clinical-CT index were determined for 90% sensitivity and 90% specificity in the development cohort, and their diagnostic performance was evaluated in the test cohort. Results: The clinical-CT index included CTL-S, body mass index, and aspartate transaminase and triglyceride concentrations. In the test cohort, the clinical-CT index (AUC, 0.81) outperformed CTL-S (0.74; p < 0.001) and clinical indices (0.73-0.75; p < 0.001) in diagnosing NAFLD. A cutoff of ≥ 46 had a sensitivity of 89% and a specificity of 41%, whereas a cutoff of ≥ 56.5 had a sensitivity of 57% and a specificity of 89%. Conclusion: The clinical-CT index is more accurate than CTL-S and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.

• Korean Journal of Radiology
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• v.21 no.8
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

• 대한예방의학회:학술대회논문집
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• 2001.10a
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• pp.142-145
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• 2001

• 대한예방의학회:학술대회논문집
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• 2005.10a
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• pp.13-30
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• 2005

• 대한예방의학회:학술대회논문집
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• 2002.10a
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• pp.67-67
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• 2002

• 대한예방의학회:학술대회논문집
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• 2003.04a
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• pp.41-51
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• 2003

• 대한예방의학회:학술대회논문집
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• 2003.04a
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• pp.68-72
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• 2003

• Clinical and Experimental Reproductive Medicine
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• v.2 no.2
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• pp.37-40
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• 1975

• 대한생식의학회:학술대회논문집
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• 2006.11a
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• pp.109-110
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• 2006