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Having ever used tobacco is a key surveillance metric. Existing tobacco use survey items differ in their inclusion of minimal use language, such as "even one or two puffs." This study aimed to quantify how minimal use language affects tobacco ever use prevalence estimated from adolescent surveys. Participants (N=5127) in the 2022 Teens, Nicotine, and Tobacco Project online panel survey of California adolescents (ages 12-17) were randomized to one of two differently worded ever use survey items (i.e., with or without minimal use language) for eight different tobacco products independently. For seven of the eight products (except hookah), minimal use language resulted in numerically higher ever use prevalence estimates. Averaged across all products, ever use prevalence was 0.7-percentage points higher when items included minimal use language (95% CI: 0.1, 1.4). Findings suggest that minimal use language yields modestly higher tobacco use prevalence, with implications for comparing and interpretating surveillance data.
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http://dx.doi.org/10.1080/13645579.2024.2320144 | DOI Listing |
Acta Neurochir (Wien)
September 2025
Department of Neurosurgery, Istinye University, Istanbul, Turkey.
Background: Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context.
View Article and Find Full Text PDFAdv Pharm Bull
July 2025
Department of Telecommunications & Systems Engineering, Universitat Autònoma de Barcelona, Sabadell, 08202, Spain.
Purpose: This study explores the potential of generative AI models to aid experts in developing scripts for pharmacokinetic (PK) models, with a focus on constructing a two-compartment population PK model using data from Hosseini et al.
Methods: Generative AI tools ChatGPT v3.5, Gemini v2.
J Gen Intern Med
September 2025
Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR, USA.
Background: Hypertension is the most prevalent reversible risk for cardiovascular morbidity and mortality. Blood pressure (BP) control is poor nationally and varies by race/ethnicity, and there is minimal understanding of the impact of country of origin.
Objective: To examine racial/ethnic disparities in BP control among high-risk patients and among Latino patients disaggregated by country of origin.
J Vis Exp
August 2025
School of Cyberspace Security, Zhengzhou University.
In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States.
We estimated linear mixed-effects models to analyze changes in language patterns (as measured using Linguistic Inquiry and Word Count) among neurodiverse youth to introduce a novel assessment useful for research into the potential benefits of special interests while minimizing respondent and researcher burden.
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