# Converting Annotated Clinical Cases into Structured Case Report Forms

arXiv: https://arxiv.org/abs/2506.11666 · HTML: https://mlsanity.com/paper/2506.11666/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://mlsanity.com/paper/2405.17428/) (2024)
- [Parallel Heuristics for Scalable Community Detection](https://mlsanity.com/paper/1410.1237/) (2014)
- [Low-resource Information Extraction with the European Clinical Case Corpus](https://mlsanity.com/paper/2503.20568/) (2025)
- [Use of 'off-the-shelf' information extraction algorithms in clinical informatics: a feasibility study of MetaMap annotation of Italian medical notes](https://mlsanity.com/paper/2104.00975/) (2021)

## Built on by (most cited first, 5 of 5)

- [Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian](https://mlsanity.com/paper/2602.17475/) (2026)
- [Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department](https://mlsanity.com/paper/2602.23062/) (2026)
- [LLM StructCore: Schema-Guided Reasoning Condensation and Deterministic Compilation](https://mlsanity.com/paper/2604.20560/) (2026)
- [eCREAM-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian](https://mlsanity.com/paper/2606.12569/) (2026)
- [sebis at CRF Filling 2026: A Two-Stage Local LLM Pipeline for Medical CRF Filling](https://mlsanity.com/paper/2606.13082/) (2026)

## Abstract (arXiv)

Case Report Forms (CRFs) are largely used in medical research as they ensure accuracy, reliability, and validity of results in clinical studies. However, publicly available, wellannotated CRF datasets are scarce, limiting the development of CRF slot filling systems able to fill in a CRF from clinical notes. To mitigate the scarcity of CRF datasets, we propose to take advantage of available datasets annotated for information extraction tasks and to convert them into structured CRFs. We present a semi-automatic conversion methodology, which has been applied to the E3C dataset in two languages (English and Italian), resulting in a new, high-quality dataset for CRF slot filling. Through several experiments on the created dataset, we report that slot filling achieves 59.7% for Italian and 67.3% for English on a closed Large Language Models (zero-shot) and worse performances on three families of open-source models, showing that filling CRFs is challenging even for recent state-of-the-art LLMs. We release the datest at https://huggingface.co/collections/NLP-FBK/e3c-to-crf-67b9844065460cbe42f80166