  Screening prioritisation in medical systematic reviews aims to rank the set
of documents retrieved by complex Boolean queries. Prioritising the most
important documents ensures that subsequent review steps can be carried out
more efficiently and effectively. The current state of the art uses the final
title of the review as a query to rank the documents using BERT-based neural
rankers. However, the final title is only formulated at the end of the review
process, which makes this approach impractical as it relies on ex post facto
information. At the time of screening, only a rough working title is available,
with which the BERT-based ranker performs significantly worse than with the
final title. In this paper, we explore alternative sources of queries for
prioritising screening, such as the Boolean query used to retrieve the
documents to be screened and queries generated by instruction-based generative
large-scale language models such as ChatGPT and Alpaca. Our best approach is
not only viable based on the information available at the time of screening,
but also has similar effectiveness to the final title.
