Purpose: Whether celiac plexus neurolysis (CPN) retains opioid-sparing efficacy in palliative care patients receiving modern extended-release opioids remains uncertain after a recent randomized trial that reported no benefit. We describe fluoroscopy-guided CPN in such patients. Methods: Thirteen consecutive palliative care patients with unresectable or metastatic upper abdominal cancer (seven pancreatic, five hepatobiliary, one gastric) on strong opioid therapy underwent posterior fluoroscopy-guided CPN with absolute ethanol after a diagnostic lidocaine block. Daily oral morphine equivalent (OME) and Numeric Rating Scale (NRS) pain scores were recorded at baseline and 1, 2, and 4 weeks. Pain response was defined a priori as ≥30% NRS reduction. Results: Median age was 67 years, baseline median OME 86.5 mg/day (range, 15~215), and median NRS 6. Median OME decreased to 72, 60, and 57.5 mg/day at 1, 2, and 4 weeks (median reductions 46.5%, 26.7%, 43.5%;all P≤0.002), and median NRS improved to 3, 4, and 4 (all P≤0.001). A pain responsewas achieved in 8/13 (61.5%) at 1 week, 7/13 (53.8%) at 2 weeks, and 5/12 (41.7%) at 4weeks. Opioid-related adverse events declined numerically (nausea, 7/13→3/12; constipation, 4→2; somnolence, 2→0; all McNemar P>0.10). No procedural complication occurred(0/13). Conclusion CPN was followed by reduction in opioid use and pain scores, withresolution of opioid-related symptoms and no complications. Although limited by the smallsample size, these findings suggest a potential role for CPN in palliative care patients receiving extended-release opioids and warrant prospective investigation.

Background: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to ensure transparency, reproducibility, and ethical applications in clinical research. Existing reporting standards are limited by their focus on specific study designs. We aimed to develop a comprehensive set of guidelines and a checklist for reporting studies that develop and utilize AI models in healthcare, covering all essential components of AI research regardless of the study design. Methods: Two experts in statistics from the Statistical Round of the Korean Journal of Anesthesiology developed these guidelines and checklist. The key elements essential for AI model reporting were identified and organized into structured sections, including study design, data preparation, model training and evaluation, ethical considerations, and clinical implementation. Iterative reviews and feedback from clinicians and researchers were used to finalize the guidelines and checklist. Results: These guidelines provide a detailed description of each item on the checklist, ensuring comprehensive reporting of AI model research. Full details regarding the AI model specifications and data-handling processes are provided. Conclusions These guidelines and checklist are meant to serve as valuable tools for researchers, addressing key aspects of AI reporting, and thereby supporting the reliability, accountability, and ethical use of AI in healthcare research.

Background: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to ensure transparency, reproducibility, and ethical applications in clinical research. Existing reporting standards are limited by their focus on specific study designs. We aimed to develop a comprehensive set of guidelines and a checklist for reporting studies that develop and utilize AI models in healthcare, covering all essential components of AI research regardless of the study design. Methods: Two experts in statistics from the Statistical Round of the Korean Journal of Anesthesiology developed these guidelines and checklist. The key elements essential for AI model reporting were identified and organized into structured sections, including study design, data preparation, model training and evaluation, ethical considerations, and clinical implementation. Iterative reviews and feedback from clinicians and researchers were used to finalize the guidelines and checklist. Results: These guidelines provide a detailed description of each item on the checklist, ensuring comprehensive reporting of AI model research. Full details regarding the AI model specifications and data-handling processes are provided. Conclusions These guidelines and checklist are meant to serve as valuable tools for researchers, addressing key aspects of AI reporting, and thereby supporting the reliability, accountability, and ethical use of AI in healthcare research.

Background: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to ensure transparency, reproducibility, and ethical applications in clinical research. Existing reporting standards are limited by their focus on specific study designs. We aimed to develop a comprehensive set of guidelines and a checklist for reporting studies that develop and utilize AI models in healthcare, covering all essential components of AI research regardless of the study design. Methods: Two experts in statistics from the Statistical Round of the Korean Journal of Anesthesiology developed these guidelines and checklist. The key elements essential for AI model reporting were identified and organized into structured sections, including study design, data preparation, model training and evaluation, ethical considerations, and clinical implementation. Iterative reviews and feedback from clinicians and researchers were used to finalize the guidelines and checklist. Results: These guidelines provide a detailed description of each item on the checklist, ensuring comprehensive reporting of AI model research. Full details regarding the AI model specifications and data-handling processes are provided. Conclusions These guidelines and checklist are meant to serve as valuable tools for researchers, addressing key aspects of AI reporting, and thereby supporting the reliability, accountability, and ethical use of AI in healthcare research.

Background: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to ensure transparency, reproducibility, and ethical applications in clinical research. Existing reporting standards are limited by their focus on specific study designs. We aimed to develop a comprehensive set of guidelines and a checklist for reporting studies that develop and utilize AI models in healthcare, covering all essential components of AI research regardless of the study design. Methods: Two experts in statistics from the Statistical Round of the Korean Journal of Anesthesiology developed these guidelines and checklist. The key elements essential for AI model reporting were identified and organized into structured sections, including study design, data preparation, model training and evaluation, ethical considerations, and clinical implementation. Iterative reviews and feedback from clinicians and researchers were used to finalize the guidelines and checklist. Results: These guidelines provide a detailed description of each item on the checklist, ensuring comprehensive reporting of AI model research. Full details regarding the AI model specifications and data-handling processes are provided. Conclusions These guidelines and checklist are meant to serve as valuable tools for researchers, addressing key aspects of AI reporting, and thereby supporting the reliability, accountability, and ethical use of AI in healthcare research.

In this paper, we review the use of hypofractionated radiotherapy for gastrointestinal malignancies, focusing on primary and metastatic liver cancer, and recurrent rectal cancer. Technological advancements in radiotherapy have facilitated the direct delivery of high-dose radiation to tumors, while limiting normal tissue exposure, supporting the use of hypofractionation. Hypofractionated radiotherapy is particularly effective for primary and metastatic liver cancer where high-dose irradiation is crucial to achieve effective local control. For recurrent rectal cancer, the use of stereotactic body radiotherapy offers a promising approach for re-irradiation, balancing efficacy and safety in patients who have been administered previous pelvic radiotherapy and in whom salvage surgery is not applicable. Nevertheless, the potential for radiation-induced liver disease and gastrointestinal complications presents challenges when applying hypofractionation to gastrointestinal organs. Given the lack of universal consensus on hypofractionation regimens and the dose constraints for primary and metastatic liver cancer, as well as for recurrent rectal cancer, this review aims to facilitate clinical decision-making by pointing to potential regimens and dose constraints, underpinned by a comprehensive review of existing clinical studies and guidelines.

Hypofractionated radiotherapy (RT) has become a trend in the modern era, as advances in RT techniques, including intensity-modulated RT and image-guided RT, enable the precise and safe delivery of high-dose radiation. Hypofractionated RT offers convenience and can reduce the financial burden on patients by decreasing the number of fractions. Furthermore, hypofractionated RT is potentially more beneficial for tumors with a low α/β ratio compared with conventional fractionation RT. Therefore, hypofractionated RT has been investigated for various primary cancers and has gained status as a standard treatment recommended in the guidelines. In genitourinary (GU) cancer, especially prostate cancer, the efficacy, and safety of various hypofractionated dose schemes have been evaluated in numerous prospective clinical studies, establishing the standard hypofractionated RT regimen. Hypofractionated RT has also been explored for gynecological (GY) cancer, yielding relevant evidence in recent years. In this review, we aimed to summarize the representative evidence and current trends in clinical studies on hypofractionated RT for GU and GY cancers addressing several key questions. In addition, the objective is to offer suggestions for the available dose regimens for hypofractionated RT by reviewing protocols from previous clinical studies.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed. Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making. Conclusions This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

Several recent studies have investigated the use of hypofractionated radiotherapy (HFRT) for various cancers. However, HFRT for non-small cell lung cancer (NSCLC) with or without concurrent chemotherapy is not yet widely used because of concerns about serious side effects and the lack of evidence for improved treatment results. Investigations of HFRT with concurrent chemotherapy in NSCLC have usually been performed in single-arm studies and with a small number of patients, so there are not yet sufficient data. Therefore, the Korean Society for Radiation Oncology Practice Guidelines Committee planned this review article to summarize the evidence on HFRT so far and provide it to radiation oncology clinicians. In summary, HFRT has demonstrated promising results, and the reviewed data support its feasibility and comparable efficacy for the treatment of locally advanced NSCLC. The incidence and severity of esophageal toxicity have been identified as major concerns, particularly when treating large fraction sizes. Strategies, such as esophagus-sparing techniques, image guidance, and dose constraints, may help mitigate this problem and improve treatment tolerability. Continued research and clinical trials are essential to refine treatment strategies, identify optimal patient selection criteria, and enhance therapeutic outcomes.