YouTube video by Neil's Cookery

Join us at Neil’s Cookery, where we create nutritious, homemade meals for dogs, cats, birds, fish, and people. Our step-by-step videos make it easy to prepare balanced, species-specific meals using re...

YouTube video by Julia Dispatch

Thieme E-Books & E-Journals

What are animals saying? Researchers are finally building the tools to understand.

Large language model-based agentic systems can process input information, plan and decide, recall and reflect, interact and collaborate, leverage various tools and act. This opens up a wealth of oppor...

Support care Navigators with digital tools that integrate openly available datasets and APIs. Enhance patient care and support systems.

Large language models such as OpenAI’s o1 have electrified the debate over achieving artificial general intelligence, or AGI. But they are unlikely to reach this milestone on their own.

Large language models such as OpenAI’s o1 have electrified the debate over achieving artificial general intelligence, or AGI. But they are unlikely to reach this milestone on their own.

Background: Disparities in cervical cancer (CC) screening exist within racial/ethnic minority and immigrant groups. However, few studies have explored the joint influence of race/ethnicity and immigrant status on screening, and the disparities that have been identified by existing studies remain incompletely explained. This study aims to identify the joint influence of race/ethnicity and immigrant status on CC screening and elucidate the barriers contributing to identified disparities. Methods: A cross-sectional analysis of 25,660 U.S. women from the 2005, 2010, and 2015 National Health Interview Surveys was done. The CC screening up-to-date status of cases was analyzed by race/ethnicity and immigrant status using logistic regression models. Conceptualized mediators were added to models to identify their contribution to identified disparities. Results: All immigrants had lower screening odds than U.S.-born non-Hispanic White women with foreign-born non-Hispanic Asians having the lowest odds (adjusted odds ratio [aOR]: 0.36, 95% confidence interval [CI]: 0.26–0.49) followed by foreign-born non-Hispanic White (aOR: 0.52, 95% CI: 0.36–0.76), Hispanic/Latinx (aOR: 0.58, 95% CI: 0.47–0.73), and non-Hispanic Black women (aOR: 0.62, 95% CI: 0.38–0.99). Adjusting for only socioeconomic status or access to care attenuated the aOR: for foreign-born Hispanic/Latinx and non-Hispanic Black women only. Adjusting simultaneously for language and acculturation attenuated the aOR: for all immigrants. Conclusions: Disparities in CC screening were only found in the immigrant populations of various racial/ethnic groups. Targeting insurance and health care access may address disparities in immigrant Hispanic/Latinx and non-Hispanic Black women. Focusing on culturally and linguistically competent care and education may be more crucial for immigrant non-Hispanic Asian and White women.

Description TLab has summer internship opportunities in data science and health AI. As a summer research intern in the Tatonetti Lab at Cedars-Sinai you will conduct research to improve drug safety an...

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. I...

Using AI code generation software is straightforward, available for many programming languages and accessible to both developers and non-developers.

Patient recruitment is challenging for clinical trials. Here, the authors introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models.