ReviewEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
The role of generative artificial intelligence in deciding fusion treatment of lumbar degeneration: a comparative analysis and narrative review.
Review 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, 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
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.
- Large language models in the vertical integration of spine surgery workflow: a scoping review.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 · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study analyzed responses and readability of generative artificial intelligence (AI) models to questions and recommendations from the 2014 Journal of Neurosurgery: Spine (JNS) guidelines for fusion procedures in the treatment of degenerative lumbar spine disease.
methodsTwenty-four questions were generated from JNS guidelines and asked to ChatGPT 4o, Perplexity, Microsoft Copilot, and Gemini. Answers were "concordant" if the response highlighted all points from the JNS guidelines; otherwise, answers were considered "non-concordant" and further sub-categorized as either "insufficient" or "overconclusive." Responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, Simple Measure of Gobbledygook (SMOG) Index, and Flesch Reading Ease test.
resultsChatGPT 4o had the highest concordance rate at 66.67%, with non-concordant responses distributed at 16.67% for both insufficient and over-conclusive classifications. Perplexity displayed a 58.33% concordance rate, with 25% insufficient and 16.67% over-conclusive responses. Copilot showed 50% concordance, with 37.5% over-conclusive and 16.67% insufficient responses. Gemini demonstrated 54.17% concordance, with 20.83% insufficient and 25% over-conclusive responses. The Flesch-Kincaid Grade Level scores ranged from 14.03 (Copilot) to 15.66 (Perplexity). The Gunning Fog Index scores varied between 15.15 (Copilot) and 18.13 (Perplexity). The SMOG Index scores ranged from 14.69 (Copilot) to 16.49 (Perplexity). The Flesch Reading Ease scores were low across all models, with Copilot showing the highest score of 20.71.
conclusionsChatGPT 4.0 emerged as the best-performing model in terms of concordance, while Perplexity displayed the highest complexity in text readability. AI can be a valuable adjunct in clinical decision-making but cannot replace clinician judgment.
Indexed as
Identifiers
40562990What 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.