Author

F. Dennstädt

3 papers indexed here

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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

Review Open access Aug 2026

The consensus-based CINEX guideline for reporting clinical information extraction studies

Abstract Objective Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical transl...

Daniel Reichenpfader, Jamil Zaghir, E. Cécilia-Joseph et al. · JAMIA Journal of the America... · 0 citations

Open access Aug 2026

A multi-factor machine learning model for predicting and characterizing clinical trial failures

About 15% of clinical trials terminate prematurely (fail), causing financial losses and delaying treatment development. This study utilized a subset of interventional trial records from the 471,252 studies registered in ClinicalTrials.gov until November 2023 to develop a clinical trial failure risk assessment machine l...

N. Cihoric, Stojan Gavric, F. Dennstädt et al. · Discover Artificial Intellig... · 0 citations