Publications by authors named "J Barbour"
Considerable efforts have been devoted to addressing the problem of conflicts of interest (COI) in health research, policy, education, and practice. An overwhelming body of evidence demonstrates that conflicts associate with deleterious outcomes for the biomedical research enterprise. Nevertheless, little has changed for research, specifically, since the Institute of Medicine's landmark was published over a decade ago.
View Article and Find Full Text PDFBased on early evidence, risk communication scholars have come to believe that longer (360-character maximum) mobile public warning messages generate more compliance than shorter (90-character maximum) messages. This study used an experimental design to test that premise. The study measured participants' ( = 481) likelihood of compliance in response to a mock Wireless Emergency Alert (WEA) message, as well as alternatives to immediate compliance: seeking additional information, taking non-recommended action, or ignoring the message.
View Article and Find Full Text PDFAmino acids are essential building blocks in biology and chemistry. Whereas nature relies on a small number of amino acid structures, chemists desire access to a vast range of structurally diverse analogues. The selective modification of amino acid side-chain residues represents an efficient strategy to access non-canonical derivatives of value in chemistry and biology.
View Article and Find Full Text PDFArticle Synopsis
- Excision repair cross-complementation group 2 (ERCC2) is key for DNA repair, and mutations in this gene are found in about 10% of bladder cancer cases, potentially indicating how well patients respond to cisplatin therapy.
- * In a study, mutations in ERCC2 were found to independently predict prognosis for bladder cancer and significantly change the mutation patterns in the genome, leading to specific mutation hotspots.
- * Researchers used these findings to create a machine learning model that may help predict harmful ERCC2 mutations, aiming to improve treatment strategies for bladder cancer patients.
Background: Large language models (LLMs) have the potential to support promising new applications in health informatics. However, practical data on sample size considerations for fine-tuning LLMs to perform specific tasks in biomedical and health policy contexts are lacking.
Objective: This study aims to evaluate sample size and sample selection techniques for fine-tuning LLMs to support improved named entity recognition (NER) for a custom data set of conflicts of interest disclosure statements.