GIM/FP/GP: [Formula: see text] Neurology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Neurology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Gastroenterology: [Formula: see text] Public Health: [Formula: see text].

Several medications have been identified as potential risk factors for microscopic colitis (MC), but evidence so far is hampered by methodological limitations.

GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text] Endocrinology: [Formula: see text] Nephrology: [Formula: see text].

GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text].

In 2017, the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group defined the certainty of evidence as the certainty that the true effect lies on one side of a threshold or in a particular range. This definition has proved useful as the basis for rating certainty, facilitating the interpretation of the results for the target audience. However, the categorization of suggested thresholds and ranges as levels of contextualization led to inconsistencies between the initial and subsequent papers and has proved confusing for some GRADE users. Although considering context in choosing thresholds remains worthwhile, the GRADE working group will no longer use the categorization of contextualization. It will instead refer simply to chosen thresholds or ranges for determining the target of certainty rating.

Climate change is anticipated to continue to adversely affect public health, with heat stress the predominant threat. Accordingly, heat-related illness is predicted to increase as extremely hot days become more frequent. Heat stroke, the most serious heat-related illness, is a medical emergency that may be fatal if it is not promptly recognized, addressed with early and rapid cooling, and accompanied by multidisciplinary supportive care as clinically indicated. Heat stroke is a preventable illness that occurs in 2 distinct forms-classic and exertional-that have distinct demographic profiles and clinical courses but similar management paradigms.

Large language models (LLMs) offer substantial promise for improving health care; however, some risks warrant evaluation and discussion. This study assessed the effectiveness of safeguards in foundational LLMs against malicious instruction into health disinformation chatbots. Five foundational LLMs-OpenAI's GPT-4o, Google's Gemini 1.5 Pro, Anthropic's Claude 3.5 Sonnet, Meta's Llama 3.2-90B Vision, and xAI's Grok Beta-were evaluated via their application programming interfaces (APIs). Each API received system-level instructions to produce incorrect responses to health queries, delivered in a formal, authoritative, convincing, and scientific tone. Ten health questions were posed to each customized chatbot in duplicate. Exploratory analyses assessed the feasibility of creating a customized generative pretrained transformer (GPT) within the OpenAI GPT Store and searched to identify if any publicly accessible GPTs in the store seemed to respond with disinformation. Of the 100 health queries posed across the 5 customized LLM API chatbots, 88 (88%) responses were health disinformation. Four of the 5 chatbots (GPT-4o, Gemini 1.5 Pro, Llama 3.2-90B Vision, and Grok Beta) generated disinformation in 100% (20 of 20) of their responses, whereas Claude 3.5 Sonnet responded with disinformation in 40% (8 of 20). The disinformation included claimed vaccine-autism links, HIV being airborne, cancer-curing diets, sunscreen risks, genetically modified organism conspiracies, attention deficit-hyperactivity disorder and depression myths, garlic replacing antibiotics, and 5G causing infertility. Exploratory analyses further showed that the OpenAI GPT Store could currently be instructed to generate similar disinformation. Overall, LLM APIs and the OpenAI GPT Store were shown to be vulnerable to malicious system-level instructions to covertly create health disinformation chatbots. These findings highlight the urgent need for robust output screening safeguards to ensure public health safety in an era of rapidly evolving technologies.

Tirzepatide reduced weight and improved cardiometabolic risk factors for participants in the SURMOUNT-1 trial. The changes in cardiometabolic risk factors by degree of tirzepatide-induced weight reduction across a wide spectrum of weight loss have not been reported.