ArticleShoulder & elbow2024
Appraisal of ChatGPT's responses to common patient questions regarding Tommy John surgery.
Article in Shoulder & elbow, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Article
- Evaluating large language model responses to patient questions on ulnar collateral ligament repair.Clinics in shoulder and elbow · 2026Article
- Evaluating Artificial Intelligence-Generated Responses to Patient Questions Regarding Orthobiologic Injections.Orthopaedic journal of sports medicine · 2026Article
- Appraisal of ChatGPT's responses to common patient questions regarding acromioclavicular joint dislocations.JSES reviews, reports, and techniques · 2025Article
- Assessing the accuracy and readability of ChatGPT 4.0's original and simplified responses to common patient questions regarding periacetabular osteotomy.Journal of experimental orthopaedics · 2025Article
- Evaluation of Large Language Models' Concordance With Guidelines on Olfaction.Laryngoscope investigative otolaryngology · 2025Article
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
Background: Artificial intelligence (AI) has progressed at a fast pace. ChatGPT, a rapidly expanding AI platform, has several growing applications in medicine and patient care. However, its ability to provide high-quality answers to patient questions about orthopedic procedures such as Tommy John surgery is unknown. Our objective is to evaluate the quality of information provided by ChatGPT 3.5 and 4.0 in response to patient questions regarding Tommy John surgery. Methods: Twenty-five patient questions regarding Tommy John surgery were posed to ChatGPT 3.5 and 4.0. Readability was assessed via Flesch Kincaid Reading Ease, Flesh Kinkaid Grade Level, Gunning Fog Score, Simple Measure of Gobbledygook, Coleman Liau, and Automated Readability Index. The quality of each response was graded using a 5-point Likert scale. Results: ChatGPT generated information at an educational level that greatly exceeds the recommended level. ChatGPT 4.0 produced slightly better responses to common questions regarding Tommy John surgery with fewer inaccuracies than ChatGPT 3.5. Conclusion: Although ChatGPT can provide accurate information regarding Tommy John surgery, its responses may not be easily comprehended by the average patient. As AI platforms become more accessible to the public, patients must be aware of their limitations.
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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.