No abstract available.
Lung*

According to the central limit theorem, the means of a random sample of size, n, from a population with mean, µ, and variance, σ², distribute normally with mean, µ, and variance, σ²/n. Using the central limit theorem, a variety of parametric tests have been developed under assumptions about the parameters that determine the population probability distribution. Compared to non-parametric tests, which do not require any assumptions about the population probability distribution, parametric tests produce more accurate and precise estimates with higher statistical powers. However, many medical researchers use parametric tests to present their data without knowledge of the contribution of the central limit theorem to the development of such tests. Thus, this review presents the basic concepts of the central limit theorem and its role in binomial distributions and the Student's t-test, and provides an example of the sampling distributions of small populations. A proof of the central limit theorem is also described with the mathematical concepts required for its near-complete understanding.
Mathematical Concepts ; Normal Distribution ; Statistical Distributions

In data analysis, given that various statistical methods assume that the distribution of the population data is normal distribution, it is essential to check and test whether or not the data satisfy the normality requirement. Although the analytical methods vary depending on whether or not the normality is satisfied, inconsistent results might be obtained depending on the analysis method used. In many clinical research papers, the results are presented and interpreted without checking or testing normality. According to the central limit theorem, the distribution of the sample mean satisfies the normal distribution when the number of samples is above 30. However, in many clinical studies, due to cost and time restrictions during data collection, the number of samples is frequently lower than 30. In this case, a proper statistical analysis method is required to determine whether or not the normality is satisfied by performing a normality test. In this regard, this paper discusses the normality check, several methods of normality test, and several statistical analysis methods with or without normality checks.
Data Collection ; Methods ; Normal Distribution ; Statistics as Topic

The survival data and the survival analysis are the data and analysis methods used to study the probability of survival. The survival data consist of a period from the juncture of a start event to the juncture of the end event (occurrence event). The period is called the survival period or survival time. In this way, the method of analysing the survival time of subjects and appropriately summarizing the degree of survival is called survival analysis. To understand and analyse survival analysis methods, researchers must be aware of some concepts. Concepts to be aware of in the survival analysis include events, censored data, survival period, survival function, survival curve and so on. This review focuses on the terms and concepts used in the survival analysis. It will also cover the types of survival data that should be collected and prepared when using actual survival analysis method and how to prepare them.
Methods ; Survival Analysis

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.

Purpose: This study aimed to report the results of femorofemoral bypass (FFB) using a great saphenous vein (GSV) graft as an alternative to polytetrafluoroethylene (PTFE) grafts. Materials and Methods: From January 2012 to December 2021, 168 patients who underwent FFB (PTFE, 143; GSV, 25) were included. The patients’ demographic features and surgical intervention results were retrospectively reviewed. Results: There were no intergroup differences in patients’ demographic features.In GSV vs. PTFE grafts, the superficial femoral artery provided statistically significant inflow and outflow (P<0.001 for both), and redo bypass was more common (P=0.021). The mean follow-up duration was 24.7±2.3 months. The primary patency rates at 3 and 5 years were 84% and 74% for PTFE grafts and 82% and 70% for GSV grafts, respectively. There was no significant intergroup difference in primary patency (P=0.661) or clinically driven target lesion revascularization (CD-TLR)-free survival (P=0.758). Clinical characteristics, disease details, and procedures were analyzed as risk factors for graft occlusion. Multivariate analysis revealed that none of the factors was associated with an increased risk of FFB graft occlusion. Conclusion FFB using PTFE or GSV grafts is a useful method with an approximately 70% 5-year primary patency rate. The GSV and PTFE grafts showed no difference in primary patency or CD-TLR–free survival during follow-up; however, FFB using GSV may be an option in selective situations.

Background: With the growth of the Internet, social media platforms have emerged as major sources of medical information. We assessed the reliability, quality, and accuracy of the most-viewed YouTube videos containing information on the effect of vitamin C on the common cold. Methods: The YouTube videos were searched on August 1, 2022, using the keywords: (“ascorbic acid” OR “vitamin C” OR “Sodium Ascorbate” OR “L-ascorbic”) AND “common cold”. The 30 most-viewed videos were included in our study. The reliability and quality of the videos were analyzed using modified DISCERN and Global Quality Scales, respectively. When the videos included at least one correct or inaccurate scientific statement about the effect of vitamin C on the common cold, they were classified as accurate or misleading videos, respectively; those without any pertinent information were considered neither accurate nor misleading. If a video contained both accurate and inaccurate statements, it was classified as misleading. Results: Of the 30 most-viewed videos, 73% were unreliable, and 67% contained misleading information and were of a poor quality. Of these 30 videos, 14 videos were produced and posted by customers who were not specialized in medicine or nutrition. Moreover, these videos were of significantly lower reliability, quality, and accuracy than those produced by nutrition or fitness channels or by medical or nutrition professionals. Conclusion The reliability, quality, and accuracy of videos uploaded by non-professionals were low. Therefore, video creators should upload reliable, high-quality videos to ensure the dissemination of accurate medical information.