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April 20, 2026ESMO Real World Data and Digital OncologyOpen Access

Accelerating real-world data collection using large language models in rare neoplasms: a bone sarcoma example

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Authors

PTP. TeteryczSRS. RynkunBSB. Szostakowski

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Overview

Evaluates large language models for extracting data from medical notes in bone sarcoma patients, indicating potential for improving clinical research efficiency.

Key Points

  • The aim is to assess the performance of small large language models in extracting structured information from Polish medical notes of bone sarcoma patients.
  • Evaluated multiple small large language models as information extractors on Polish medical notes.
  • Select electronic health records of 302 bone sarcoma patients from 2016 to 2022.
  • Annotated key variables: pathology type, tumor size, localization, grade, and primary resection.
  • Implemented ensemble voting strategies to enhance data extraction accuracy.
  • Single-model accuracy ranged from 17.5% to 30.3%, highly dependent on prompts.
  • Accuracy for tumor localization reached up to 36.2%.
  • Majority of extracted non-concordant values were non-valid.
  • The ensemble approach significantly improved accuracy to 83.6%, peaking at 90.0% for resection type.

Cite This Study

Teterycz et al. (2026) studied this question.

synapsesocial.com/papers/69e5c2d003c2939914028bf9https://doi.org/10.1016/j.esmorw.2026.100705
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