Si Won Lee
Yonsei University
13 Papers
Si Won Lee is an academic researcher from Yonsei University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Marked Loss of Muscle, Visceral Fat, or Subcutaneous Fat After Gastrectomy Predicts Poor Survival in Advanced Gastric Cancer: Single-Center Study from the CLASSIC Trial
Hyung Soon Park,Hyung Soon Park,Hyo Song Kim,Seung Hoon Beom,Sun Young Rha,Hyun Cheol Chung,Jee Hung Kim,You Jin Chun,Si Won Lee,Eun Ah Choe,Su Jin Heo,Sung Hoon Noh,Woo Jin Hyung,Jae Ho Cheong,Hyoung Il Kim,Taeil Son,Joon Seok Lim,Song Ee Baek,Minkyu Jung +18 more
TL;DR: Marked loss in body composition parameters significantly predicted shorter DFS and OS among patients with GC who underwent gastrectomy, suggesting postoperative nutrition and active healthcare interventions could improve the prognosis of these GC patients.
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Performance of clinician prediction of survival in oncology outpatients with advanced cancer
Yu Jung Kim,Seokhyun Yoon,Sang-Yeon Suh,Yusuke Hiratsuka,Beodeul Kang,Si Won Lee,Hong Yup Ahn,Koung Jin Suh,Jiwon Kim,Se Hyun Kim,Jin Won Kim,Keun Wook Lee,Jee Hyun Kim,Jong-Seok Lee +13 more
TL;DR: The overall accuracy of clinician prediction of survival (CPS) in predicting 12-week to 48-week survival was high in medical oncology outpatients, however the sensitivity of 12- week CPS was low and prognostic confidence was not associated with the accuracy.
Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort
Yu Jung Kim,Hayeon Lee,Ho Geol Woo,Si Won Lee,Moonki Hong,Eun Hee Jung,Shin Hye Yoo,Jinseok Lee,Dong Keon Yon,Beodeul Kang +9 more
TL;DR: This study developed a machine learning model to predict delirium in patients with advanced cancer using a multicenter, patient-based registry cohort in South Korea, achieving 68.83% sensitivity and 70.85% specificity with sex as the top contributor to prediction.
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Performance of mid-upper arm circumference and other prognostic indices based on inflammation and nutrition in oncology outpatients: a tertiary cancer center study.
Yu Jung Kim,Yusuke Hiratsuka,Sang-Yeon Suh,Seonae Won,Eun Hee Jung,Beodeul Kang,Si Won Lee,Hong Yup Ahn,Koung Jin Suh,Jiwon Kim,Se Hyun Kim,Jin Won Kim,Keun Wook Lee,Jee Hyun Kim,Jong-Seok Lee +14 more
TL;DR: Inflammation and nutrition-based prognostic indices showed similar acceptable accuracies in estimating the 12- and 24-week survival of oncology outpatients and a simple and non-invasive index MUAC showed comparable performance with established indices including GPS and mGPS.
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Pre-screening of patient-reported symptoms using the Edmonton Symptom Assessment System in outpatient palliative cancer care.
Garden Lee,Han Sang Kim,Si Won Lee,Yu Rang Park,Eun Hwa Kim,Bori Lee,Youn Jung Hu,Kyung-A Kim,DooA Kim,Ho Yeon Cho,Beodeul Kang,Hye Jin Choi +11 more
TL;DR: Pre-screening of patient-reported symptoms using ESAS can be useful for identifying unmet palliative care needs in advanced cancer patients.
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