Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11031485 | PMC |
| http://dx.doi.org/10.1007/s11695-024-07212-6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ChatGPT-4o is Not a Reliable Study Source for Orthopaedic Surgery Residents.
JB JS Open Access
September 2025
Department of Orthopaedic Surgery, St. Luke's University Health Network, Bethlehem, Pennsylvania.
Background: The use of artificial intelligence platforms by medical residents as an educational resource is increasing. Within orthopaedic surgery, older Chat Generative Pre-trained Transformer (ChatGPT) models performed worse than resident physicians on practice examinations and rarely answered questions with images correctly. The newer ChatGPT-4o was designed to improve these deficiencies but has not been evaluated.
View Article and Find Full Text PDFThe PGPB paradox: A critical review of field performance and practical constraints in agriculture.
World J Microbiol Biotechnol
September 2025
Department of Microbiology, University of Georgia, Athens, GA, 30602, USA.
While PGPB have historically been applied in agriculture, their formal recognition in the last century has driven intensive research into their role as sustainable tools for improving crop yield and stress tolerance. As they are primarily sourced from wild or native environments, the widespread enthusiasm has led to heightened expectations surrounding their potential, often based on the assumption that biological solutions are inherently safer and more effective than synthetic inputs. However, despite their popularity, increasing reports of inconsistent or limited performance under real-world, field conditions have raised critical questions about their credibility as biofertilizers and biocontrol agents.
View Article and Find Full Text PDFAssessment of Past Dioxin Emissions from Waste Incineration Plants Based on Archive Studies and Process Modeling: A New Methodological Tool.
Arch Environ Contam Toxicol
September 2025
Ecole Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering, 1015, Lausanne, Switzerland.
Pollution from past industrial activities can remain unnoticed for years or even decades because the pollutant has only recently gained attention or been identified by measurements. Modeling the emission history of pollution is essential for estimating population exposure and apportioning potential liability among stakeholders. This paper proposes a novel approach for reconstructing the history of polychlorinated dibenzo-p-dioxin (PCDD) and polychlorinated dibenzofuran (PCDF) pollution from municipal solid waste incinerators (MSWIs) with unknown past emissions.
View Article and Find Full Text PDFThe presentation of self in the age of ChatGPT.
Front Sociol
August 2025
Laboratory of Anthropology of Contemporary Worlds (LAMC), Faculty of Philosophy and Social Sciences, Institute of Sociology, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Contemporary debates about artificial intelligence (AI) still treat automation as a straightforward substitution of human labor by machines. Drawing on Goffman's dramaturgical sociology, this paper reframes AI in the workplace as rather than automation. We argue that the central-but routinely overlooked-terrain of struggle is symbolic-interactional: workers continuously stage, conceal, and re-negotiate what counts as "real" work and professional competence.
View Article and Find Full Text PDFGenerative artificial intelligence for automated data extraction from unstructured medical text.
JAMIA Open
October 2025
Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, United States.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.
Materials And Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry.