ArticleCureus2024
ChatGPT-4.0 vs. Google: Which Provides More Academic Answers to Patients' Questions on Arthroscopic Meniscus Repair?
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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
Purpose The purpose of this study was to evaluate the ability of a Chat Generative Pre-trained Transformer (ChatGPT) to provide academic answers to frequently asked questions using a comparison with Google web search FAQs and answers. This study attempted to determine what patients ask on Google and ChatGPT and whether ChatGPT and Google provide factual information for patients about arthroscopic meniscus repair. Method A cleanly installed Google Chrome browser and ChatGPT were used to ensure no individual cookies, browsing history, other side data, or sponsored sites. The term "arthroscopic meniscus repair" was entered into the Google Chrome browser and ChatGPT. The first 15 frequently asked questions (FAQs), answers, and sources of answers to FAQs were identified from both ChatGPT and Google search engines. Results Timeline of recovery (20%) and technical details (20%) were the most commonly asked question categories of a total of 30 questions. Technical details and timeline of recovery questions were more commonly asked on ChatGPT compared to Google (technical detail: 33.3% vs. 6.6%, p=0.168; timeline of recovery: 26.6% vs. 13.3%, p=0.651). Answers to questions were more commonly from academic websites in website categories in ChatGPT compared to Google (93.3% vs. 20%, p=0.0001). The most common answers to frequently asked questions were academic (20%) and commercial (20%) in Google. Conclusion Compared to Google, ChatGPT provided significantly fewer references to commercial content and offered responses that were more aligned with academic sources. ChatGPT may be a valuable adjunct in patient education when used under physician supervision, ensuring information aligns with evidence-based practices.
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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.