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Electronic Survey Methodology for Data Collection and Analysis in Nutrition and Dietetics Research

BACKGROUND

This article is part of a series of articles in the Journal of the Academy of Nutrition and Dietetics exploring the importance of research design, epidemiological methods, and statistical analysis as applied to nutrition and dietetics research. The purpose of this ongoing statistical portfolio is to assist Registered Dietitian Nutritionists (RDN) and Nutrition and Dietetic Technicians, Registered (NDTR) in interpreting nutrition research and applying scientific principles to produce high-quality data analysis. Advances in technology are promoting faster, easier, and often more diverse data collection and analysis. Consumers and practitioners alike are rapidly adopting electronic communication preferences (ie, telehealth, mobile applications, social media). This article, which accompanies the companion article on basic survey research, is an overview of electronic internet-mediated survey methodology for data collection and analysis in nutrition and dietetics research. Its purpose is to highlight the unique requirements in electronic planning and administration for surveys that builds on basic survey principles. This includes the effect of internet-mediated data methodology on an array of research parameters, including evaluation of software functions for the investigator and survey navigation issues for the participant. A Checklist for Reporting Electronic Survey Statistics (CRESS) is provided as a guide for data dissemination in nutrition and dietetics research.

Chain of Thought Utilization in Large Language Models and Application in Nephrology

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.