Evidence map›Paper›PMID 42539543›Full record

ArticleFrontiers in public health2026

Evaluating large language models as tools for public health education on scoliosis.

Yi Chen Wang, Xuanyan Liu, Mengjie Chen, Fangchun Jin, Xiaohan Wu, Yi Luo

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In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yi Chen Wang *Department of Orthopedics, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Xuanyan Liu *Department of Administration, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Mengjie ChenDepartment of Orthopedics, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Fangchun JinDepartment of Pediatric Orthopedics, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaohan WuDepartment of Pediatric Surgery, Zhaotong First People's Hospital, Zhaotong, Yunnan, China.
Yi LuoDepartment of Orthopedics, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study was aimed to compare the efficacy of three most popular large language models (LLMs)-Claude Opus 4.6, ChatGPT Thinking 5.4 and DeepSeek v3.2 in answering frequently asked questions (FAQs) about scoliosis. Methods: 20 scoliosis related questions (four categories, five questions in each category) were submitted to each LLM. A panel of 9 experts (two spine surgeons, two pediatric orthopedic surgeons and five physical therapists, all blinded to the LLMs and responses) rated independently each response generated by LLMs on a 6 points Likert scale (1 as strongly disagree to 6 as strongly agree). 540 total ratings were collected. Intergroup comparisons were conducted by Kruskal Wallis test and Mann Whitney U pairwise tests. Paired question level analysis was achieved by Friedman test and Wilcoxon signed rank comparisons. Results: Claude's score was 5.53 ± 0.76 much higher than both ChatGPT (4.84 ± 0.86, Conclusion: Although all three LLMs achieved favorable overall ratings (>4.8/6), Claude performed significantly better than ChatGPT and DeepSeek for scoliosis FAQs taking into account its higher accuracy and consistency. Within the scope of the present evaluation, Claude demonstrated the strongest overall performance among the three LLMs tested.

Indexed as

Health EducationLarge Language ModelsPublic HealthScoliosisHumansartificial intelligencefrequently asked questionslarge language modelsscoliosissociety on scoliosis orthopedic and rehabilitation treatment

Identifiers

PMID42539543
PMCPMC13423767

What OpenQuestion holds

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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.