Purpose: This study aimed to develop a comprehensive practical guide for enteral nutrition (EN) designed to enhance patient safety and reduce complications in Korea. Under the leadership of the Korean Society for Parenteral and Enteral Nutrition (KSPEN), the initiative sought to standardize EN procedures, improve decision-making, and promote effective multidisciplinary communication. Methods: The KSPEN EN committee identified key questions related to EN practices and organized them into seven sections such as prescribing, delivery route selection, formula preparation, administration, and quality management. Twenty-one experts, selected based on their expertise, conducted a thorough literature review to formulate evidence-based recommendations. Drafts underwent peer review both within and across disciplines, with final revisions completed by the KSPEN Guideline Committee. The guide, which will be published in three installments, addresses critical elements of EN therapy and safety protocols. Results: The practical guide recommends that EN orders include detailed elements and advocates the use of electronic medical records for communication. Standardized prescription forms and supplementary safety measures are outlined. Review frequency is adjusted according to patient condition—daily for critically ill or unstable patients and as dictated by institutional protocols for stable patients. Evidence indicates that adherence to these protocols reduces mortality, complications, and prescription errors. Conclusion The KSPEN practical guide offers a robust framework for the safe delivery of EN tailored to Korea’s healthcare context. It emphasizes standardized protocols and interdisciplinary collaboration to improve nutritional outcomes, patient safety, and operational efficiency. Rigorous implementation and monitoring of adherence are critical for its success.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: To evaluate the relationship between distal fusion level in correction and fusion surgery for adolescent idiopathic scoliosis (AIS) and radiologic changes in the sacroiliac (SI) joint. Materials and Methods: This retrospective cohort study evaluated patients who underwent correction and fusion for AIS between 2005 and 2017 with at least 5 years of follow-up. We categorized patients into two groups: Group 1 (distal fusion above L2, 74 patients) and Group 2 (distal fusion at L3 and below, 52 patients). Radiologic parameters and SI joint changes were evaluated on plain radiographs obtained from preoperative to 5 years postoperatively. We also investigated other risk factors for SI joint change. Results: Analysis of demographic factors revealed no significant difference between the two groups. There was a significant difference in the incidence of SI joint change between Group 1 (5 patients, 6.75%) and Group 2 (18 patients, 34.61%), with Group 2 showing a faster increase in incidence according to the Kaplan-Meier method (p<0.0001). Preoperative lumbar lordosis (LL) and ΔLL had a significant relationship with SI joint changes [preoperative LL, hazard ratio (HR)=0.77, 95% confidence interval (CI)=0.64– 0.93, p=0.008; ΔLL, HR=0.79, 95% CI=0.67–0.95, p=0.01). Conclusion After AIS surgery, patients who had fusion to the lower lumbar vertebrae (L3 or L4) experienced a higher incidence and faster progression of degenerative changes in the SI joint. Low preoperative LL and inadequate correction of LL during the operation were also risk factors for SI joint degeneration.

This study aimed to evaluate the long-term efficacy and complication of radiotherapy for benign soft tissue tumors. Five cases of benign soft tissue tumors (two plexiform neurofibromas, two juvenile nasopharyngeal angiofibromas, and one cavernous sinus hemangioma) who underwent radiotherapy were enrolled. All patients had at least 10 years of follow-up. The median follow-up duration was 12 years (range, 10 to 27). Three patients underwent incomplete excision prior to radiotherapy. Radiation doses were either 54 Gy in 30 fractions or 50.4 Gy in 28 fractions (1.8 Gy per fraction). Every patient achieved complete remission (CR) or near-CR. The tumor volume decreased significantly within the first 2 years of follow-up and continued to decrease slowly up to 10 years; no distinct further decrease in tumor volume was observed after 10 years. One patient developed left mandibular hypoplasia 8 years after radiotherapy. Significant volume decrease was achievable within a few years after radiotherapy in benign soft tissue tumors. Therefore, radiotherapy is a viable option for unresectable or incompletely resected benign soft tissue tumors with a minimum risk of complication.

Purpose: To evaluate the relationship between distal fusion level in correction and fusion surgery for adolescent idiopathic scoliosis (AIS) and radiologic changes in the sacroiliac (SI) joint. Materials and Methods: This retrospective cohort study evaluated patients who underwent correction and fusion for AIS between 2005 and 2017 with at least 5 years of follow-up. We categorized patients into two groups: Group 1 (distal fusion above L2, 74 patients) and Group 2 (distal fusion at L3 and below, 52 patients). Radiologic parameters and SI joint changes were evaluated on plain radiographs obtained from preoperative to 5 years postoperatively. We also investigated other risk factors for SI joint change. Results: Analysis of demographic factors revealed no significant difference between the two groups. There was a significant difference in the incidence of SI joint change between Group 1 (5 patients, 6.75%) and Group 2 (18 patients, 34.61%), with Group 2 showing a faster increase in incidence according to the Kaplan-Meier method (p<0.0001). Preoperative lumbar lordosis (LL) and ΔLL had a significant relationship with SI joint changes [preoperative LL, hazard ratio (HR)=0.77, 95% confidence interval (CI)=0.64– 0.93, p=0.008; ΔLL, HR=0.79, 95% CI=0.67–0.95, p=0.01). Conclusion After AIS surgery, patients who had fusion to the lower lumbar vertebrae (L3 or L4) experienced a higher incidence and faster progression of degenerative changes in the SI joint. Low preoperative LL and inadequate correction of LL during the operation were also risk factors for SI joint degeneration.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.