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Nutrition Users' Guides: RCTs Part 2 - structured guide for interpreting and applying study results from randomised controlled trials on therapy or prevention questions.
BMJ Nutr Prev Health
August 2024
Department of Nutrition, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA.
This article continues from a prior commentary on evaluating the risk of bias in randomised controlled trials addressing nutritional interventions. Having provided a synopsis of the risk of bias issues, we now address how to understand trial results, including the interpretation of best estimates of effect and the corresponding precision (eg, 95% CIs), as well as the applicability of the evidence to patients based on their unique circumstances (eg, patients' values and preferences when trading off potential desirable and undesirable health outcomes and indicators (eg, cholesterol), and the potential burden and cost of an intervention). Authors can express the estimates of effect for health outcomes and indicators in relative terms (relative risks, relative risk reductions, OR or HRs)-measures that are generally consistent across populations-and absolute terms (risk differences)-measures that are more intuitive to clinicians and patients.
View Article and Find Full Text PDFCommentary: Effective communication about pregnancy, birth, lactation, breastfeeding and newborn care: the importance of sexed language.
Front Glob Womens Health
January 2025
Research Centre for Healthcare and Communities, Coventry University, Coventry, United Kingdom.
Medical Imaging Data Strategies for Catalyzing AI Medical Device Innovation.
J Imaging Inform Med
January 2025
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) technologies for patient care. Breakthrough innovations in data-driven AI technologies are founded on seamless communication between data providers, data managers, data users and regulators or other evaluators to determine the standards for quality data. However, the complexity in imaging data quality and heterogeneous nature of AI-enabled medical devices and their intended uses presents several challenges limiting the clinical translation of novel AI technologies.
View Article and Find Full Text PDFQuantitative approaches to structural intersectionality research: Conceptual and methodologic considerations.
Soc Sci Med
January 2025
Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
There has been a recent influx in the development of novel measures of structural forms of discrimination, including structural racism, xenophobia, sexism, heterosexism, and cisgenderism. These systems of power and oppression are inherently interdependent and mutually constitutive, yet a paucity of research has investigated their joint impacts; this gap is likely reflective of the limited guidance that exists regarding how to effectively combine multiple measures of structural discrimination to examine their joint impacts on population health and health inequities. In this commentary, we seek to redress this by describing conceptual and methodologic considerations for population health researchers interested in conducting quantitative structural intersectionality research - an intersectionality-informed research approach focused on examining how systems of power and oppression intersect to shape population health and health inequities.
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