Results from Randomized Controlled Trials (RCTs) establish the comparative
effectiveness of interventions, and are in turn critical inputs for
evidence-based care. However, results from RCTs are presented in (often
unstructured) natural language articles describing the design, execution, and
outcomes of trials; clinicians must manually extract findings pertaining to
interventions and outcomes of interest from such articles. This onerous manual
process has motivated work on (semi-)automating extraction of structured
evidence from trial reports. In this work we propose and evaluate a
text-to-text model built on instruction-tuned Large Language Models (LLMs) to
jointly extract Interventions, Outcomes, and Comparators (ICO elements) from
clinical abstracts, and infer the associated results reported. Manual (expert)
and automated evaluations indicate that framing evidence extraction as a
conditional generation task and fine-tuning LLMs for this purpose realizes
considerable ($\sim$20 point absolute F1 score) gains over the previous SOTA.
We perform ablations and error analyses to assess aspects that contribute to
model performance, and to highlight potential directions for further
improvements. We apply our model to a collection of published RCTs through
mid-2022, and release a searchable database of structured findings (anonymously
for now): bit.ly/joint-relations-extraction-mlhc

本研究建议并评估了基于指令调优的大型语言模型的文本 - to - 文本模型，以共同从临床摘要中提取干预、结局和对照元素（ICO 元素）并推断所报道的相关结果。