ArticleAnnals of medicine and surgery (2012)2025
Evaluation of the performance of large language models in endoscopic lumbar surgery: a comparative analysis.
Article in Annals of medicine and surgery (2012), 2025. 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
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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.
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to evaluate and compare the performance of three large language models (LLMs)-ChatGPT o1-preview, Claude 3.5 Sonnet, and Gemini 1.5 Pro-in providing information on endoscopic lumbar surgery based on 10 frequently asked patient questions. Methods: The 10 high-frequently asked patient questions about endoscopic lumbar surgery were selected through discussion among authors. These questions were then submitted to the three LLMs. Responses were evaluated by five spine surgeons using a 5-point Likert scale for overall quality, text readability, content relevance, and humanistic care. Additionally, five non-medical volunteers assessed the understandability and satisfaction of the responses. Results: The intraclass correlation coefficients of ChatGPT o1-preview, Claude 3.5 Sonnet, and Gemini 1.5 Pro of the five evaluators were 0.522, 0.686, and 0.512, respectively. Claude 3.5 Sonnet received the highest scores for overall quality (4.86 ± 0.35, Conclusion: From the perspective of professional surgeons, Claude 3.5 Sonnet provided the highest quality and most relevant information. However, ChatGPT o1-preview was more understandable and satisfactory for non-professional users. This study not only highlights the potential of LLMs in patient education but also emphasizes the need for careful consideration of their role in medical practice, including technical limitations and ethical issues.
Identifiers
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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.