---
title: "DocUA at CRF Filling 2026 — paper by Zabolotnii (2026)"
description: "The design guarantees valid output, works across inference backends, and needs no… 0.63 F1 on Codabench. 19 references."
authors: ["Serhii Zabolotnii"]
year: 2026
venue: "Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026"
source: "https://aclanthology.org/2026.cl4health-1.33/"
doi: "10.63317/4ar36j4w3vrk"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zabolotnii-2026-docua-crf-filling-2026-llm/"
---
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# DocUA at CRF Filling 2026: LLM StructCore –- Schema-Guided Reasoning Condensation and Deterministic Compilation

**Authors:** Serhii Zabolotnii · **Year:** 2026 · **Venue:** [Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/cl4health/) ([Workshop on Patient-Oriented Language Processing @ LREC 2026](https://md.research.elamur.ai/free/en/venues/cl4health/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.cl4health-1.33/) · [PDF](https://aclanthology.org/2026.cl4health-1.33.pdf) · [DOI 10.63317/4ar36j4w3vrk](https://doi.org/10.63317/4ar36j4w3vrk) · [OpenAlex](https://openalex.org/W7166530028)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This system description addresses the CL4Health 2026 shared task of filling a 134-item Dyspnea Case Report Form (CRF) from English and Italian clinical notes. Most fields in a given note are empty, and the scorer penalizes both missed values and invented ones. Instead of asking one LLM to output all 134 fields at once, the author splits the work into two stages, based on Schema-Guided Reasoning (SGR). In Stage 1, an LLM writes a compact JSON summary organized under nine fixed clinical domains. In Stage 2, a rule-based compiler with no LLM calls turns that summary into the official format. It parses key-value lines, standardizes item names (with an optional UMLS alias map), removes poorly supported positives through evidence checks, and maps values onto the controlled vocabulary. On the dev80 split, a Mistral Large 3 front end reached macro-F1 of 0.6543 (EN) and 0.6905 (IT). The submitted English run scored 0.63 on the hidden test set. The design guarantees valid output, works across inference backends, and needs no language-specific engineering.

## Key points
- **Data:** the official NLP-FBK/dyspnea-crf-* splits (10 train, 80 dev, 200 hidden test), plus 2,667 unannotated notes (NLP-FBK/dyspnea-clinical-notes) used for teacher generation. The median dev document has only about 12 non-unknown items out of 134.
- **Stage 2 compiler:** applies 70+ abbreviation rules and a UMLS alias map covering 134/134 items. It uses regex evidence gates for high-risk items such as active neoplasia, arrhythmia and acute coronary syndrome, and normalizes values into 13 vocabulary categories.
- **Format stability:** JSON parsing succeeded on every input without grammar-constrained decoding, even with a quantized 4B model (MedGemma) served through llama.cpp.
- **Teacher comparison:** Mistral Large 3 and Qwen 3.5 found the same number of true positives on EN dev (236). Qwen produced more false positives (211 vs. 170).
- **Test result:** 0.63 F1 on Codabench, within 0.05 of the top team, Aurum (0.68). The code is released as the llm_structcore package.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Cherkasy State Business College](https://ror.org/035bemc82).

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🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

## Cite

Cite as: Serhii Zabolotnii. 2026. DocUA at CRF Filling 2026: LLM StructCore –- Schema-Guided Reasoning Condensation and Deterministic Compilation. Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zabolotnii2026docua,
  title = {{DocUA at CRF Filling 2026: LLM StructCore –- Schema-Guided Reasoning Condensation and Deterministic Compilation}},
  author = {Serhii Zabolotnii},
  booktitle = {Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026},
  year = {2026},
  doi = {10.63317/4ar36j4w3vrk},
  url = {https://doi.org/10.63317/4ar36j4w3vrk}
}
```

- [← previous DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures](https://md.research.elamur.ai/free/en/papers/uthayasooriyar-2026-docpolarbert-pre-trained-model-document/)
- [next → Document Overlap Is Not Evidence Continuity: Measuring Retrieval Jitter in Citation-Based RAG Evaluation](https://md.research.elamur.ai/free/en/papers/ponnuraj-2026-document-overlap-is-not-evidence/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/30/)

## References

19 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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15. Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Jeff Huang, Chuyue Sun, Cody Hao Yu, Shiyi Cao, Christos Kober, Joseph E. Gonzalez, Clark Barrett, Ying Sheng, and Ion Stoica. 2024. [SGLang: Efficient execution of structured language model programs](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/). arXiv preprint arXiv:2312.07104. 12.
16. Language Resource References Anthony McEnery and others. 2004. The EMILLE/CIIL Corpus. EMILLE (Enabling Minority Language Engineering) Project. distributed via ELRA: ELRA-Id W0037, ISLRN 039-846-040-604-0. [find](https://scholar.google.com/scholar?q=Language+Resource+References+Anthony+McEnery+and+others%2E+2004%2E+The+EMILLE%2FCIIL+Corpus%2E+EMILLE+%28Enabling+Minority+Language+Engineering%29+Project%2E+distributed+via+ELRA%3A+ELRA%2DId+W0037%2C+ISLRN+039%2D846%2D040%2D6)
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*Updated 2 November 2026*