Accessibility settings

This paper is in the following e-collection/theme issue:

Cancer and Clinical Trials (77) Digital Health Reporting Standards, Quality and Transparency in e-Research (223) Health and Risk Communication (147) Bots and AI Approaches to Detect and Counter Misinformation (29) AI Language Models in Health Care (738) Academic Professionalism and Ethical Practices (13) Misinformation in Scientific Publishing (12) Transparent Reporting (21) AI in Peer Review and Research Assessment (9) AI for Research Integrity (4)

Published on in Vol 12 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78221, first published .
Nurse wearing mask shows tablet to woman with headscarf, possibly cancer patient.

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

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

Authors of this article:

Carole Koechli1, 2 Author Orcid Image ;   Fabio Dennstädt2 Author Orcid Image ;   Christina Schröder1, 2 Author Orcid Image ;   Daniel M Aebersold2 Author Orcid Image ;   Robert Förster1, 2 Author Orcid Image ;   Daniel R Zwahlen1 Author Orcid Image ;   Paul Windisch1, 2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Priya M, Jasrotia S, Abia A. Public Health Risks of Pathogenic Bacteria in Freshwater Bodies: A Review of Quantitative Microbial Risk Assessment Approaches and Applications. Limnological Review 2026;26(1):10 View
  2. Windisch P, Koechli C, Dennstädt F, Aebersold D, Zwahlen D, Förster R, Schröder C. 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. JMIR AI 2026;5:e95565 View