The goal of this guidance is to provide recommendations and suggestions that encourage fairness, equity, consistency, and clarity in use and reporting of race and ethnicity in medical and science journals. As previously summarized, "terminology, usage, and word choice are critically important, especially when describing people and when discussing race and ethnicity. Inclusive language supports diversity and conveys respect. Language that imparts bias toward or against persons or groups based on characteristics or demographics must be avoided." 1 With the publication of an earlier version of this guidance, 1 comments were invited, and helpful assessments and comments were received from numerous reviewers, scholars, and researchers, who provided valuable feedback and represented diverse expertise and opinions. After thorough review of these comments (some of which did not agree with others) and additional research and discussion, the guidance was revised and updated, and additional formal review was obtained. In this Editorial, we present the updated guidance, and we sincerely thank the many reviewers for their contributions, each of whom are listed in the Acknowledgment at the end of this article.This guidance continues to acknowledge that race and ethnicity are social constructs as well as the important sensitivities and controversies related to use of these terms and associated nomenclature in medical and health research, education, and practice. Thus, for content published in medical journals, language and terminology must be accurate, clear, and precise, and must reflect fairness, equity, and consistency in use and reporting of race and ethnicity. The guidance also acknowledges that the reporting of race and ethnicity should not be considered in isolation and should be accompanied by reporting of other sociodemographic factors and social determinants, including concerns about racism, disparities, and inequities, and the intersectionality of race and ethnicity with these other factors.The guidance defines commonly used terms associated with race and ethnicity and acknowledges that these terms and definitions have changed, that some are out of date, and that the nomenclature will continue to evolve. Other topics addressed include relevant concerns and controversies in health care and research, including the intersectionality of ancestry and heritage, social determinants of health, and other socioeconomic, structural, institutional, cultural, and demographic factors; reporting of race and ethnicity in research articles; use of racial and ethnic collective or umbrella terms, capitalization, and abbreviations; listing racial and ethnic categories in alphabetical order vs order by majority; adjectival vs noun usage for categories of race and ethnicity; geo-Opinion EDITORIAL
Box 1 | Prevailing attitudes of medical professionals emerging from public review and participant survey Agreement with goal of standardizing nomenclature, with acknowledgment of challenges Regarded multiplicity of terms and lack of adherence to established definitions as confusing and potentially leading to errors Anticipated that a standardized nomenclature would help foster consistency in trial design, execution, and reporting Judged consistency between terms used in scholarly and patient communities to be an important goal, but not one overriding the need for precision and efficiency Journal editors strongly agreed that having a more standardized nomenclature for kidney disease would be useful for their journals, but they anticipated time constraints of journal personnel to be the biggest barrier to implementation Qualified endorsement of replacing "renal" with "kidney" Felt that foregrounding "kidney" would be easier for patients and their families Perceived a greater likelihood of raising awareness, attracting funding, and influencing public policy with consistent use of "kidney" Cautioned against a wholesale switch because "renal" may be less awkward in some contexts and may be necessary in others (e.g., ESRD as a CMS definition) Dissatisfaction with "end-stage" as a descriptor of kidney disease Recognized that this wording can be demoralizing and stigmatizing for patients Considered the implication of imminent death to be outdated Frustrated by imprecision in its use (ranging from being a synonym for dialysis patients to a descriptor of patients with kidney failure with or without kidney replacement therapy) Recognition of the need for ongoing attention to nomenclature issues Noted that standardization of nomenclature is dependent on uptake of consensus definitions B where definitions are in flux or are more contentious, standardization of that nomenclature set may be premature B enhancing adoption of definitions requires continued effort Highlighted the need for harmonization with ongoing, broader-scope ontology efforts Expected that improved understanding of molecular mechanisms will lead to more-precise definitions and nomenclature CMS, Centers for Medicare & Medicaid Services; ESRD, end-stage renal disease.
Artificial intelligence (AI) technologies to help authors improve the preparation and quality of their manuscripts and published articles are rapidly increasing in number and sophistication. These include tools to assist with writing, grammar, language, references, statistical analysis, and reporting standards. Editors and publishers also use AI-assisted tools for myriad purposes, including to screen submissions for problems (eg, plagiarism, image manipulation, ethical issues), triage submissions, validate references, edit, and code content for publication in different media and to facilitate postpublication search and discoverability. 1 In November 2022, OpenAI released a new open source, natural language processing tool called ChatGPT. 2,3 ChatGPT is an evolution of a chatbot that is designed to simulate human conversation in response to prompts or questions (GPT stands for "generative pretrained transformer"). The release has prompted immediate excitement about its many potential uses 4 but also trepidation about potential misuse, such as concerns about using the language model to cheat on homework assignments, write student essays, and take examinations, including medical licensing examinations. 5 In January 2023, Nature reported on 2 preprints and 2 articles published in the science and health fields that included ChatGPT as a bylined author. 6 Each of these includes an affiliation for ChatGPT, and 1 of the articles includes an email address for the nonhuman "author." According to Nature, that article's inclusion of ChatGPT in the author byline was an "error that will soon be corrected." 6 However, these articles and their nonhuman "authors" have already been indexed in PubMed and Google Scholar.Nature has since defined a policy to guide the use of large-scale language models in scientific publication, which prohibits naming of such tools as a "credited author on a research paper" because "attribution of authorship carries with it accountability for the work, and AI tools cannot take such responsibility." 7 The policy also advises researchers who use these tools to document this use in the Methods or Acknowledgment sections of manuscripts. 7 Other journals 8,9 and organizations 10 are swiftly developing policies that ban inclusion of these nonhuman technologies as "authors" and that range from prohibiting the inclusion of AI-generated text in submitted work 8 to requiring full transparency, responsibility, and accountability for how such tools are used and reported in scholarly publication. 9,10 The International Conference on Machine Learning, which issues calls for papers to be reviewed and discussed at its conferences, has also announced a new policy: "Papers that include text generated from a large-scale language model (LLM) such as ChatGPT are
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