We present the CanSA system for the MedEx-ACT@ACL 2026 shared task, which requires extracting and classifying clinical decisions from ICU discharge summaries into nine DIC-TUM categories. We have developed three approaches: (1) a training-free system which consists of a preprocessing module that normalizes text and an inference engine combining zero shot LLMs with a RAG ensemble, (2) a supervised fine-tuning method which required training, and (3) a training-free retrieval-augmented pipeline employing TF–IDF-based lexical retrieval to surface in-context exemplars from the development corpus, combined with section aware chunking and structured extraction calls to a large language model. Our team’s best submission achieved a Final Score of 0.41, ranking 34th out of 37 on the official test leaderboard.

This paper focuses on the real world application of scientific writing and on determining rhetorical moves, an important step in establishing the argument structure of biomedical articles. Using the observation that the structure of scholarly writing in laboratory-based experimental sciences closely follows laboratory procedures, we examine most closely the Methods section of the texts and adopt an approach of identifying rhetorical moves that are procedure-oriented. We also propose a verb-centric frame semantics with an effective set of semantic roles in order to support the analysis. These components are designed to support a computational model that extends a promising proposal of appropriate rhetorical moves for this domain, but one which is merely descriptive. Our work also contributes to the understanding of argument-related annotation schemes. In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset. The annotated dataset that we produce provides the important knowledge needed for our ultimate goal of analyzing biochemistry articles.