ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Are large language models such as ChatGPT, capable of supporting patients and general practitioners after spine surgery?
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
purposeTo evaluate whether large language models (LLMs) can provide accurate, complete, and audience-adapted answers to common spine-surgery-related questions for patients and family practitioners.
methodsTen frequently asked spine-surgery questions were collected at a level 1 trauma center and simplified linguistically. Five LLMs (ChatGPT, Claude 3.5 Sonnet, Gemini Advanced 1.5 Pro, Copilot Pro, and DeepSeek V3) were queried using zero-shot prompting with persona-specific instructions for family practitioners and middle-aged patients. Responses were assessed by spine surgeons and non-medical raters for correctness, completeness, adaptability, and empathy using five-point Likert scales. Readability was quantified using the Flesch Reading Ease Score (FRES).
resultsAll LLMs generated largely correct and usable responses. ChatGPT and Claude showed the highest correctness and completeness, particularly for practitioner-directed answers. Gemini and Copilot achieved superior readability and empathy for patient-facing responses. DeepSeek demonstrated balanced performance across all domains. Readability differed substantially between practitioner- and patient-oriented outputs.
conclusionLLMs can support communication and education following spine surgery when used with structured prompting. Clinical oversight remains essential to mitigate risks related to inaccuracies and hallucinations. LEVEL OF EVIDENCE: III.
Indexed as
Identifiers
42606695What 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.