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January 24, 20263 citationsOpen Access

Large Language Models for Supporting Clear Writing and Detecting Spin in Randomized Controlled Trials in Oncology: Comparative Analysis of GPT Models and Prompts.

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CKCarole KoechliFDFabio DennstädtCSChristina Schröder

Key Points

  • The research aims to explore the ability of large language models to detect spin in oncology randomized controlled trials.
  • Employed large language models to analyze trial reporting language.
  • Compared conclusions with full abstracts to identify discrepancies.
  • Conducted a feasibility study on recognized trials.
  • Large language models effectively identified potential spin in trial reports.
  • Noted discrepancies are significant in enhancing transparency.
  • Recommendations indicate the need for further development to handle complex trials.

Abstract

LLMs can effectively detect potential spin in oncology RCT reporting by identifying discrepancies between how trials are presented in the conclusions vs the full abstracts. This approach could serve as a supplementary tool for improving transparency in scientific reporting, although further development is needed to address more complex trial designs beyond those examined in this feasibility study.

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Cite This Study

Koechli et al. (2026) studied this question.

synapsesocial.com/papers/69746126bb9d90c67120aff0https://doi.org/10.2196/78221
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