  Identifying critical research within the growing body of academic work is an
essential element of quality research. Systematic review processes, used in
evidence-based medicine, formalise this as a procedure that must be followed in
a research program. However, it comes with an increasing burden in terms of the
time required to identify the important articles of research for a given topic.
In this work, we develop a method for building a general-purpose filtering
system that matches a research question, posed as a natural language
description of the required content, against a candidate set of articles
obtained via the application of broad search terms. Our results demonstrate
that transformer models, pre-trained on biomedical literature then fine tuned
for the specific task, offer a promising solution to this problem. The model
can remove large volumes of irrelevant articles for most research questions.
