ArticleMissouri medicine
Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models.
Article in Missouri medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: There are barriers that exist for individuals to adhere to cardiovascular rehabilitation programs. A key driver to patient adherence is appropriately educating patients. A growing education tool is using large language models to answer patient questions. Methods: The primary objective of this study was to evaluate the readability quality of educational responses provided by large language models for questions regarding cardiac rehabilitation using Gunning Fog, Flesh Kincaid, and Flesch Reading Ease scores. Results: The findings of this study demonstrate that the mean Gunning Fog, Flesch Kincaid, and Flesch Reading Ease scores do not meet US grade reading level recommendations across three models: ChatGPT 3.5, Copilot, and Gemini. The Gemini and Copilot models demonstrated greater ease of readability compared to ChatGPT 3.5. Conclusions: Large language models could serve as educational tools on cardiovascular rehabilitation, but there remains a need to improve the text readability for these to effectively educate patients.
Indexed as
Identifiers
39958590PMC11827661What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.