Evidence map›Paper›PMID 40861605›Full record

ArticleCureus2025

Perceived Accuracy of Spine-Related Medical Advice From ChatGPT, TikTok, and the North American Spine Society Clinical Practice Guidelines.

Divya Bhatia, Michael S Kim, Melissa Romoff, Asha Timm, Emily Mills, Hao-Hua Wu, Sohaib Hashmi, Don Park, Yu-Po Lee

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Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Divya BhatiaDepartment of Research, Palos Verdes High School, Palos Verdes Estates, USA.
Michael S KimDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Melissa RomoffDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Asha TimmFielding School of Public Health, University of California Los Angeles, Los Angeles, USA.
Emily MillsDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Hao-Hua WuDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Sohaib HashmiDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Don ParkDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.
Yu-Po LeeDepartment of Orthopedic Surgery, University of California, Irvine, School of Medicine, Orange, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients increasingly turn to large language models (LLMs) and social media platforms for medical advice. The accuracy of these sources, particularly compared to peer-reviewed clinical practice guidelines, remains poorly characterized. MATERIALS AND

methodsThis cross-sectional study evaluated the perceived accuracy of spine-related medical advice generated by ChatGPT (ChatGPT (OpenAI, powered by GPT-4, San Francisco, CA, USA), TikTok (Los Angeles, CA, USA), and the North American Spine Society (NASS) clinical practice guidelines. Medical advice for four spine pathologies was collected from each source. Sixteen orthopedic surgeons rated the accuracy of excerpted recommendations on a 10-point Likert scale. Descriptive statistics summarized mean ratings and standard deviations.

resultsFor lumbar stenosis, mean (±SD) accuracy scores were 7.75 ± 2.11 for ChatGPT, 7.00 ± 1.80 for NASS, and 2.50 ± 1.54 for TikTok. For lumbar spondylolisthesis, scores were 7.56 ± 1.50 for ChatGPT, 5.94 ± 2.63 for NASS, and 5.31 ± 2.49 for TikTok. For lumbar disc herniation with radiculopathy, scores were 7.25 ± 2.13 on ChatGPT, 7.06 ± 1.55 on NASS, and 6.44 ± 2.03 on TikTok. For cervical radiculopathy, scores were 7.13 ± 1.38 for ChatGPT, 4.00 ± 2.44 for NASS, and 6.50 ± 2.12 for TikTok.

conclusionsChatGPT-generated outputs received the highest ratings for perceived accuracy. NASS guidelines, while evidence-based and peer-reviewed, remain inaccessible to most patients. Professional societies may consider adapting guideline content for dissemination via widely used digital platforms to improve public education and reduce misinformation.

Indexed as

chatgptclinical practice guidelinespatient perceptionsocial mediatiktok

Identifiers

PMID40861605
PMCPMC12377256

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Registered trials

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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.