Team Aurevia introduces a local open-weight healthcare NLP system for the CLPsych 2026 Shared Task, predicting MIND-coded self-state elements, moments of change, summaries, anddynamic signatures from social media timelines. The task is difficult because coarse presence, fine-grained ABCD subelements, and timeline-level change require different longitudinal evidence over privacy-sensitive mental-health language. Our system combines TF-IDF retrieval, schema-constrained local Qwen2.5 prompting, ordinal calibration, and conservative post-processing. Among official runs, Aurevia ranked 3rd of 17 for Task 1.2 presence prediction, 5th of 13 overall for Task 3.1, 1st on Task 3.1 consistency, and 2nd of 9 for MIND-coded deterioration signatures, showing that constrained local LLM pipelines can remain competitive in sensitive healthcare NLP while reducing reliance on hosted proprietary inference.