AIMC Topic: Cross-Sectional Studies

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Markers of body fat, the mediating role of alanine aminotransferase, and their association with the risk of metabolic dysfunction-associated steatotic liver disease.

European journal of pediatrics
Metabolic dysfunction-associated steatotic liver disease (MASLD) in children with obesity correlates with metabolic dysfunction, yet interactions between anthropometrics, liver enzymes, and risk of MASLD remain unclear. This study included 219 childr...

METS-VF as a novel predictor of gallstones in U.S. adults: a cross-sectional analysis (NHANES 2017-2020).

BMC gastroenterology
BACKGROUND AND AIMS: Obesity is a well-established risk factor for gallstone formation, but traditional anthropometric measures (e.g., BMI, waist circumference) inadequately assess metabolically active visceral adiposity. The novel Metabolic Score fo...

Evaluating the Performance of State-of-the-Art Artificial Intelligence Chatbots Based on the WHO Global Guidelines for the Prevention of Surgical Site Infection: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Surgical site infection (SSI) is the most prevalent type of health care-associated infection that leads to increased morbidity and mortality and a significant economic burden. Effective prevention of SSI relies on surgeons strictly follow...

Association between atherogenic index of plasma and hypertension combined with diabetes mellitus in United States adults: an analysis of the NHANES surveys from 2011 to 2016.

Journal of health, population, and nutrition
INTRODUCTION: Observational studies have indicated that individuals with hypertension (HTN) and diabetes mellitus (DM) tend to exhibit elevated plasma atherogenic index of plasma (AIP), defined as log (triglyceride [TG]/high-density lipoprotein chole...

Predicting Engagement With Conversational Agents in Mental Health Therapy by Examining the Role of Epistemic Trust, Personality, and Fear of Intimacy: Cross-Sectional Web-Based Survey Study.

JMIR human factors
BACKGROUND: The use of conversational agents (CAs) in mental health therapy is gaining traction due to their accessibility, anonymity, and nonjudgmental nature. However, understanding the psychological factors driving preferences for CA-based therapy...

Readiness and Acceptance of Nursing Students Regarding AI-Based Health Care Technology on the Training of Nursing Skills in Saudi Arabia: Cross-Sectional Study.

JMIR nursing
BACKGROUND: The rapid advancements in artificial intelligence (AI) technologies across various sectors, including health care, necessitate the need for a comprehensive understanding of their applications. Specifically, the acceptance and readiness of...

Dentists' perception and use of AI and robotics in the care of persons with disabilities.

Scientific reports
Despite the growing role of AI and robotics in healthcare, little is known about their integration into dental care for persons with disabilities (PWDs) in Saudi Arabia. This study aimed to assess dentists' perceptions and attitudes towards and use o...

The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data.

BMC cancer
BACKGROUND: Obesity is a known risk factor for breast cancer (BC), but conventional metrics such as body mass index (BMI) may insufficiently capture central adiposity. The weight-adjusted waist index (WWI) has emerged as a potentially superior anthro...

Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.

BMC medical informatics and decision making
BACKGROUND: Hyperuricemia (HUA) as a global public health challenge, although its overall epidemiological characteristics have been widely reported, its age-specific risk pattern remains controversial. This study aims to reveal the risk factors of HU...

Evaluating Large Language Models for imaging modality selection: Potential to reduce unnecessary contrast agent use and radiation exposure.

Clinical imaging
INTRODUCTION: Large Language Models (LLMs) represent a transformative leap in artificial intelligence with the potential to revolutionize radiologic decision-making. This study uniquely evaluates the performance of various LLMs from different vendors...