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        <title>CASPAR: A Context-Aware Span Refinement Approach for Decision Support</title>
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    <abstract>This paper presents CASPAR, a two-stage approach for the MedExACT shared task on medical decision span extraction and classification from ICU discharge summaries. Stage 1 performs document-level sequence labeling using a sliding-window RoBERTa encoder with BiGRU and CRF to generate candidate spans. Stage 2 applies a lightweight refinement module that revisits each candidate within its surrounding context to revise category assignments and correct span boundaries. The system achieves a final score of 0.5668 on the official leaderboard, substantially outperforming the organizer baseline on span-level F1. In addition to system description, we provides ablation results, repeated-run validation statistics, and subgroup- and error-level analyses that highlight the challenges of exact boundary recovery and confusion in race categories subgroups in clinical decision extraction.</abstract>
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%0 Conference Proceedings
%T CASPAR: A Context-Aware Span Refinement Approach for Decision Support
%A Tao, Jing
%A Eskandari, Amir
%A Zulkernine, Farhana
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F tao-etal-2026-caspar
%X This paper presents CASPAR, a two-stage approach for the MedExACT shared task on medical decision span extraction and classification from ICU discharge summaries. Stage 1 performs document-level sequence labeling using a sliding-window RoBERTa encoder with BiGRU and CRF to generate candidate spans. Stage 2 applies a lightweight refinement module that revisits each candidate within its surrounding context to revise category assignments and correct span boundaries. The system achieves a final score of 0.5668 on the official leaderboard, substantially outperforming the organizer baseline on span-level F1. In addition to system description, we provides ablation results, repeated-run validation statistics, and subgroup- and error-level analyses that highlight the challenges of exact boundary recovery and confusion in race categories subgroups in clinical decision extraction.
%U https://aclanthology.org/2026.bionlp-2.21/
%P 146-154

Markdown (Informal)

[CASPAR: A Context-Aware Span Refinement Approach for Decision Support](https://aclanthology.org/2026.bionlp-2.21/) (Tao et al., BioNLP 2026)

• CASPAR: A Context-Aware Span Refinement Approach for Decision Support (Tao et al., BioNLP 2026)

ACL

• Jing Tao, Amir Eskandari, and Farhana Zulkernine. 2026. CASPAR: A Context-Aware Span Refinement Approach for Decision Support. In Proceedings of the BioNLP 2026 (Shared Tasks), pages 146–154, San Diego, California, USA. Association for Computational Linguistics.