Utilising Large Language Models for the Automated Mapping of Medical Research to Translational Stages

Classification of research articles according to translational research stages enables funding bodies, academic and medical institutes, and policymakers to objectively assess the distribution of resources across the research spectrum. We aim to utilise Large Language Models (LLM) to classify medical research papers into translational research levels based on their titles and abstracts, comparing performance across a range of LLMs, multiple runs and a bag of words (BoW) baseline. We quantify the performance of open-weight LLMs against a human-labelled data set of 318 medical research papers. Using a description of translational levels, the LLMs showed good performance with an F1 score of 0.83 ahead of a baseline BoW approach of 0.68. We show that LLMs can accurately classify titles and abstracts into translational levels within a fully automated pipeline.