---
title: "Gladiator at MEDIQA-SYNUR 2026: Contextual Clinical"
description: "The authors build a hybrid pipeline: LLM extraction: Claude Opus 4 receives the full schema… 78.98% F1. 19 references, cited by 1 paper here."
authors: ["Siva Satyanarayana Raju Pusapati", "Ankit Singh"]
year: 2026
venue: "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026"
source: "https://aclanthology.org/2026.clinicalnlp-1.20/"
doi: "10.63317/2tkvpwgzzcn2"
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/pusapati-2026-gladiator-mediqa-synur-2026-contextual/"
---
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# Gladiator at MEDIQA-SYNUR 2026: Contextual Clinical Extraction: Integrating Foundation Models with Domain-Specific Validation Rules

**Authors:** Siva Satyanarayana Raju Pusapati and Ankit Singh · **Year:** 2026 · **Venue:** [Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/clinicalnlp/) ([Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/clinicalnlp/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.clinicalnlp-1.20/) · [PDF](https://aclanthology.org/2026.clinicalnlp-1.20.pdf) · [DOI 10.63317/2tkvpwgzzcn2](https://doi.org/10.63317/2tkvpwgzzcn2) · [OpenAlex](https://openalex.org/W7166537735)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/pusapati-2026-gladiator-mediqa-synur-2026-contextual/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses the MEDIQA-SYNUR 2026 shared task. The goal is to convert synthetic nurse dictation transcripts into structured EHR observations that fit a 193-concept clinical schema. The schema uses four value types: NUMERIC, STRING, SINGLE_SELECT and MULTI_SELECT. A prediction counts as correct only if both the concept ID and the value match exactly, so raw LLM output is unreliable on its own.

The authors build a hybrid pipeline:
- **LLM extraction:** Claude Opus 4 receives the full schema, three training examples and targeted guidance for difficult fields.
- **Post-processing:** six filters (for example, dropping speculative diagnoses and physiologically implausible numbers) and five normalisation steps clean the output.
- **Rule-based merging:** the cleaned output is combined with matches from over 400 context-checked regex rules and then validated against the schema.

On the development set, the system reaches 78.98% F1, with recall (84.32%) clearly higher than precision (74.27%). Most errors are spurious secondary diagnoses and confusion between near-identical cognitive status options. The work shows how rule-based guardrails can make LLM extraction more dependable for clinical documentation. The authors also note concerns about cost, privacy and reliance on synthetic data.

## Key points
- **Data:** 122 training and 101 development samples of synthetic nursing dictations, plus a held-out test set. Gold labels were created by expert nurses using an open clinical ontology.
- **Model setup:** Claude Opus 4 (ID listed as claude-opus-4-5-20251101), temperature 0.7, max_tokens 4096. Processing the dev set cost roughly $8–10.
- **Dev results:** P 74.27%, R 84.32%, F1 78.98%. All reported results come from the dev set, which was also used to tune the system.
- **Error profile:** 57% of errors are false positives. These include 32 over-extracted secondary diagnoses (e.g., past conditions such as "history of diabetes"), 12 cognitive status value errors and 10 dyspnea value errors.
- **Stated limitations:** no ablation studies, only one LLM tested, synthetic data only, and a proprietary model that hinders reproducibility and deployment in healthcare.

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

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## Cite

Cite as: Siva Satyanarayana Raju Pusapati and Ankit Singh. 2026. Gladiator at MEDIQA-SYNUR 2026: Contextual Clinical Extraction: Integrating Foundation Models with Domain-Specific Validation Rules. Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026.

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

```bibtex
@inproceedings{pusapati2026gladiator,
  title = {{Gladiator at MEDIQA-SYNUR 2026: Contextual Clinical Extraction: Integrating Foundation Models with Domain-Specific Validation Rules}},
  author = {Siva Satyanarayana Raju Pusapati and Ankit Singh},
  booktitle = {Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026},
  year = {2026},
  doi = {10.63317/2tkvpwgzzcn2},
  url = {https://doi.org/10.63317/2tkvpwgzzcn2}
}
```

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## References

19 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., and Sontag, D. (2022). [Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/). In Proceedings of EMNLP 2022, pages 1998-2022.
2. Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jin, D., Naumann, T., and McDermott, M. (2019). [Publicly available clinical BERT embeddings](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/). In Proceedings of the Clinical NLP Workshop, pages 72-78. \#\# 9.
3. Conclusion Aronson, A.R. and Lang, F.M. (2010). An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3):229-236. [find](https://scholar.google.com/scholar?q=Conclusion+Aronson%2C+A%2ER%2E+and+Lang%2C+F%2EM%2E+%282010%29%2E+An+overview+of+MetaMap%3A+historical+perspective+and+recent+advances%2E+Journal+of+the+American+Medical+Informatics+Association%2C+17%283%29%3A229%2D236%2E)
4. Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 17(5):507-513. [find](https://scholar.google.com/scholar?q=Extraction+System+%28cTAKES%29%3A+architecture%2C+component+evaluation+and+applications%2E+Journal+of+the+American+Medical+Informatics+Association%2C+17%285%29%3A507%2D513%2E)
5. Baumann, L.A., Baker, J., and Elshaug, A.G. (2018). The impact of electronic health record systems on clinical documentation times: A systematic review. Health Affairs, 37(4):655-663. [find](https://scholar.google.com/scholar?q=Baumann%2C+L%2EA%2E%2C+Baker%2C+J%2E%2C+and+Elshaug%2C+A%2EG%2E+%282018%29%2E+The+impact+of+electronic+health+record+systems+on+clinical+documentation+times%3A+A+systematic+review%2E+Health+Affairs%2C+37%284%29%3A655%2D663%2E)
6. Singhal, K., Azizi, S., Tu, T., et al. (2023). [Large language models encode clinical knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/). Nature, 620:172-180.
7. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In Advances in Neural Information Processing Systems, volume 30, pages 5998-6008.
8. Brown, T., Mann, B., Ryder, N., et al. (2020). [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). In Advances in Neural Information Processing Systems, volume 33, pages 1877-1901.
9. Wang, Y., Wang, L., Rastegar-Mojarad, M., et al. (2018). Clinical information extraction applications: A literature review. Journal of Biomedical Informatics, 77:34-49. [find](https://scholar.google.com/scholar?q=Wang%2C+Y%2E%2C+Wang%2C+L%2E%2C+Rastegar%2DMojarad%2C+M%2E%2C+et+al%2E+%282018%29%2E+Clinical+information+extraction+applications%3A+A+literature+review%2E+Journal+of+Biomedical+Informatics%2C+77%3A34%2D49%2E)
10. Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., and Buchanan, B.G. (2001). A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics, 34(5):301-310. [find](https://scholar.google.com/scholar?q=Chapman%2C+W%2EW%2E%2C+Bridewell%2C+W%2E%2C+Hanbury%2C+P%2E%2C+Cooper%2C+G%2EF%2E%2C+and+Buchanan%2C+B%2EG%2E+%282001%29%2E+A+simple+algorithm+for+identifying+negated+findings+and+diseases+in+discharge+summaries%2E+Journal+of+Biomedical+Infor)
11. Chen, Z., Chen, W., Smiley, C., Shah, S., Borber, I., and Chang, S. (2021). Schema-guided multi-domain dialogue state tracking with graph attention neural networks. In Proceedings of NAACL 2021, pages 1124-1134. [find](https://scholar.google.com/scholar?q=Chen%2C+Z%2E%2C+Chen%2C+W%2E%2C+Smiley%2C+C%2E%2C+Shah%2C+S%2E%2C+Borber%2C+I%2E%2C+and+Chang%2C+S%2E+%282021%29%2E+Schema%2Dguided+multi%2Ddomain+dialogue+state+tracking+with+graph+attention+neural+networks%2E+In+Proceedings+of+NAACL+2021%2C+pages)
12. Gu, Y., Tinn, R., Cheng, H., et al. (2021). [Domain-specific language model pretraining for biomedical natural language processing](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/). ACM Transactions on Computing for Healthcare, 3(1):1-23.
13. Harkema, H., Dowling, J.N., Thornblade, T., and Chapman, W.W. (2009). ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics, 42(5):839-851. [find](https://scholar.google.com/scholar?q=Harkema%2C+H%2E%2C+Dowling%2C+J%2EN%2E%2C+Thornblade%2C+T%2E%2C+and+Chapman%2C+W%2EW%2E+%282009%29%2E+ConText%3A+An+algorithm+for+determining+negation%2C+experiencer%2C+and+temporal+status+from+clinical+reports%2E+Journal+of+Biomedical+Info)
14. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., and Fung, P. (2023). [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Computing Surveys, 55(12):1-38.
15. Joukes, E., de Keizer, N.F., de Bruijne, M.C., Abu-Hanna, A., and Cornet, R. (2018). Impact of electronic versus paper-based recording on the quality of clinical documentation in nursing. Studies in Health Technology and Informatics, 247:261-265. [find](https://scholar.google.com/scholar?q=Joukes%2C+E%2E%2C+de+Keizer%2C+N%2EF%2E%2C+de+Bruijne%2C+M%2EC%2E%2C+Abu%2DHanna%2C+A%2E%2C+and+Cornet%2C+R%2E+%282018%29%2E+Impact+of+electronic+versus+paper%2Dbased+recording+on+the+quality+of+clinical+documentation+in+nursing%2E+Studies+in+H)
16. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., and Kang, J. (2020). [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/). Bioinformatics, 36(4):1234-1240.
17. Michalopoulos, G., Corbeil, J.P., Bader, C., Bodenstab, N., and Ben Abacha, A. (2026). Overview of the MEDIQA-SYNUR 2026 Shared Task on Synthetic Nursing Observation Extraction. In Proceedings of ClinicalNLP @ LREC 2026. [find](https://scholar.google.com/scholar?q=Michalopoulos%2C+G%2E%2C+Corbeil%2C+J%2EP%2E%2C+Bader%2C+C%2E%2C+Bodenstab%2C+N%2E%2C+and+Ben+Abacha%2C+A%2E+%282026%29%2E+Overview+of+the+MEDIQA%2DSYNUR+2026+Shared+Task+on+Synthetic+Nursing+Observation+Extraction%2E+In+Proceedings+of+Clin)
18. Rastogi, A., Zang, X., Srivastava, S., Guez, R., and Dilek, Z. (2020). [Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset](https://md.research.elamur.ai/free/en/papers/rastogi-2019-towards-scalable-multi-domain-conversational/). In Proceedings of AAAI 2020, pages 8689-8696.
19. Savova, G.K., Masanz, J.J., Ogren, P.V., et al. (2010). Mayo clinical Text Analysis and Knowledge [find](https://scholar.google.com/scholar?q=Savova%2C+G%2EK%2E%2C+Masanz%2C+J%2EJ%2E%2C+Ogren%2C+P%2EV%2E%2C+et+al%2E+%282010%29%2E+Mayo+clinical+Text+Analysis+and+Knowledge)

*Updated 31 October 2026*