Effect of electronic cigarette atomising power and flavour on aerosol size-segregated metal concentration and inhalation risk.

Introduction: Although numerous studies have estimated the inhalation dose of metals emitted from electronic cigarettes (e-cigs), the impact of factors including aerosol size and the atomising power of e-cig aerosols on estimating the inhalation dose of metals remains underexplored. A comprehensive understanding of these determinants is essential to assess the health risks associated with inhaling e-cig aerosols, which may contain potentially harmful metals.

Objectives: The aim of this study is to elucidate the mass and inhalation doses of potentially harmful metals in e-cig aerosols by different particle size and their association with the various atomising powers of e-cig devices and flavours.

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability.

Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.

Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.

The TRIPOD-LLM reporting guideline for studies using large language models.

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.

Academic burnout and its related factors among dental students in southeast Iran: a cross-sectional study.

Background And Aim: Academic burnout in students manifests as emotional exhaustion, depersonalization, and a sense of stagnation in their education. Given the high prevalence of occupational burnout among dental students, screening dental students for early signs of burnout can facilitate intervention and prevent negative effects on their physical and mental well-being.

Methods And Materials: This cross-sectional study included 180 clinical dentistry students in their third year and above at Kerman Dental Faculty during the academic year 2022-2023.

A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice.

Background: Ambient artificial intelligence offers promise for improving documentation efficiency and reducing provider burden through clinical note generation. However, challenges persist in workflow integration, compliance, and widespread adoption. This study leveraged a Learning Health System (LHS) framework to align research and operations using a hybrid effectiveness-implementation protocol, embedded as pragmatic trial operations within the electronic health record (EHR).