Evidence map›Paper›PMID 40307827›Full record

ArticleJournal of orthopaedic surgery and research2025

Assessment of ChatGPT's adherence to evidence-based clinical practice guidelines for plantar fasciitis management.

Le Zhang, Tianyi Wang, Yinfeng Zheng, Xiaochuan Kong, Gang Hong, Lei Zang

Abstract read
In one paragraph

Article in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

6 authors.

Le Zhang *Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Tianyi Wang *Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Yinfeng ZhengDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Xiaochuan KongDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Gang HongDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
Lei ZangDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China. zanglei@ccmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to test the multidimensional performance of Chat-Generative Pre-trained Transformer (ChatGPT) in generating recommendations for the management of plantar fasciitis (PF) that adhere to well-established clinical practice guidelines. MATERIALS AND

methods21 queries were raised from the 2023 APTA guideline recommendations for PF and prompted into ChatGPT-4o and ChatGPT-4 Turbo. Two experienced orthopaedic physicians evaluated the responses for accuracy, consistency, self-awareness, and fabrication and falsification using five-point Likert scales. The group-wise comparisons were conducted between the two models and subgroups.

resultsThe interrater agreement between evaluators was moderate to good (intraclass correlation coefficients of 0.573-0.757). Both versions of ChatGPT were outperformed and comparable across all dimensions, including accuracy ([4.1 ± 0.8] vs. [4.1 ± 0.7], P = 0.959), consistency ([4.6 ± 0.5] vs. [4.6 ± 0.6], P = 0.890), self-awareness ([4.3 ± 0.6] vs. [4.5 ± 0.5], P = 0.407), and fabrication and falsification ([4.6 ± 0.6] vs. [4.5 ± 0.4], P = 0.681). In the subgroup comparisons, better performance was identified in closed-ended questions and for positive rather than negative recommendations (P < 0.05). No significant differences were found between recommendation strength subgroups, except in fabrication and falsification ([4.4 ± 0.6] vs. [5.0 ± 0], P = 0.001).

conclusionsThe two mainstream versions of ChatGPT showed comparable and superior performance in generating recommendations concordant with clinical guidelines for PF management. However, notable specific issues included performance variations between different prompt strategies, recommendation grades, and recommendation type, and the models should still be utilized with caution.

Indexed as

Evidence-Based MedicineFasciitis, PlantarGuideline AdherencePractice Guidelines as TopicGenerative Artificial IntelligenceHumansArtificial intelligenceChatGPTClinical guidelinesLarge Language modelsPhysical therapyPlantar fasciitis

Identifiers

PMID40307827
PMCPMC12042459

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.