Background: Researchers who use the results of statistical analyses to draw conclusions about collected data must write a statistical analysis section in their manuscript. Describing statistical analyses in precise detail is as important as presenting the dosages of drugs and methodology of interventions. It is also essential for scientific accuracy and transparency in scientific research. Methods: We evaluated the quality of the statistical analysis sections of clinical research articles published in the Korean Journal of Anesthesiology between February 2020 and February 2021. Using a Likert scale where 1, 2, and 3 represented “not described at all,” “partially described,” and “fully described,” respectively, the following 6 items were assessed: 1) stating of the statistical analysis methods used, 2) rationale for and detailed description of the statistical analysis methods used, 3) parameters derived from the statistical analyses, 4) type and version of the statistical software package used, 5) significance level, and 6) sidedness of the test (one-sided vs. two-sided). The first 3 items evaluate issues directly related to the statistical analysis methods used and last 3 are indirectly related items. Results: In all the included articles, the statistical analysis methods used were stated (score of 3). However, only 4 articles (12.9%) fully described the sidedness of the test (score of 3). Conclusions Authors tend not to describe the sidedness of statistical analysis tests in the methodology section of clinical research articles. It is essential that the sidedness be described in research studies.

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.

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.

Background: As a side effect of interscalene brachial plexus block (ISBPB), stellate ganglion block (SGB) causes reductions in pupil size (Horner’s syndrome) and cardiac sympathetic nervous activity (CSNA). Reduced CSNA is associated with hemodynamic instability when patients are seated. Therefore, instantaneous measurements of CSNA are important in seated patients presenting with Horner’s syndrome. However, there are no effective tools to measure real-time CSNA intraoperatively. To evaluate the usefulness of pupillometry in measuring CSNA, we investigated the relationship between pupil size and CSNA. Methods: Forty-two patients undergoing right arthroscopic shoulder surgery under ISBPB were analyzed. Pupil diameters were measured at 30 Hz for 2 s using a portable pupillometer. Bilateral pupil diameters and CSNA (natural-log-transformed low-frequency power [0.04–0.15 Hz] of heart rate variability [lnLF]) were measured before ISBPB (pre-ISBPB) and 15 min after transition to the sitting position following ISBPB (post-sitting). Changes in the pupil diameter ([right pupil diameter for post-sitting – left pupil diameter for post-sitting] – [right pupil diameter for pre-ISBPB – left pupil diameter for pre-ISBPB]) and CSNA (lnLF for post-sitting – lnLF for pre-ISBPB) were calculated. Results: Forty-one patients (97.6%) developed Horner’s syndrome. Right pupil diameter and lnLF significantly decreased upon transition to sitting after ISBPB. In the linear regression model (R2 =0.242, P=0.001), a one-unit decrease (1 mm) in the extent of changes in the pupil diameter reduced the extent of changes in lnLF by 0.659 ln(ms2/Hz) (95% CI [0.090, 1.228]). Conclusions Pupillometry is a useful tool to measure changes in CSNA after the transition to sitting following ISBPB.