  This paper introduces the Pandemic PACT Advanced Categorisation Engine
(PPACE) along with its associated dataset. PPACE is a fine-tuned model
developed to automatically classify research abstracts from funded biomedical
projects according to WHO-aligned research priorities. This task is crucial for
monitoring research trends and identifying gaps in global health preparedness
and response. Our approach builds on human-annotated projects, which are
allocated one or more categories from a predefined list. A large language model
is then used to generate `rationales' explaining the reasoning behind these
annotations. This augmented data, comprising expert annotations and rationales,
is subsequently used to fine-tune a smaller, more efficient model. Developed as
part of the Pandemic PACT project, which aims to track and analyse research
funding and clinical evidence for a wide range of diseases with outbreak
potential, PPACE supports informed decision-making by research funders,
policymakers, and independent researchers. We introduce and release both the
trained model and the instruction-based dataset used for its training. Our
evaluation shows that PPACE significantly outperforms its baselines. The
release of PPACE and its associated dataset offers valuable resources for
researchers in multilabel biomedical document classification and supports
advancements in aligning biomedical research with key global health priorities.
