Author

D. Aebersold

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Open access Oct 2026

Evaluating the Automated Extraction of Radiation Oncology Trial Data at Scale from the ClinicalTrials.gov Database Using a Large Language Model.

Background The rapid growth of biomedical publishing in radiation oncology has created a need for automated methods to extract structured information from unstructured text. Large language models (LLMs) show promise in biomedical natural language processing, but their use for clinical trial data curation remains to be...

F. Dennstaedt, Lena Kretzschmar, N. Cihoric et al. · Oncology · 0 citations

Open access Sep 2026

More signal versus more noise: comparing full text and abstract as inputs for large language model-based classification of oncology trial eligibility criteria

Abstract Objectives Large language models (LLMs) offer significant potential for automating clinical trial classification by eligibility criteria. However, the optimal input data remain unclear: while abstracts provide a condensed signal, full-text articles contain substantially more information. Whether this additiona...

J. Weyrich, F. Dennstädt, Robert Förster et al. · JAMIA Open · 0 citations

Open access Jul 2026

Evidence Use and Identifier-Conditioned Prior Knowledge in Large Language Model Classification of Oncology Trials Assessed Through Progressive Content Removal and Counterfactual Testing: Comparative Analysis

Testing whether oncology randomized trial success classification is driven by abstract evidence or by identifier-conditioned prior knowledge, and whether models follow counterfactual outcome evidence when it conflicts with original trial identifiers showed that identifiers can carry predictive signal and occasionally c...

P. Windisch, C. Koechli, F. Dennstädt et al. · JMIR AI · 1 citation