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.