Evidence map›Paper›PMID 40342470›Full record

ArticleCureus2025

An Assessment of the Accuracy and Consistency of ChatGPT in the Management of Midshaft Clavicle Fractures.

Christopha J Knee, Ryan J Campbell, Brahman S Sivakumar, Michael J Symes

Abstract read
In one paragraph

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

4 authors.

Christopha J KneeDepartment of Orthopaedics and Trauma Surgery, Royal North Shore Hospital, Sydney, AUS.
Ryan J CampbellDepartment of Orthopaedics and Trauma Surgery, Royal North Shore Hospital, Sydney, AUS.
Brahman S SivakumarDepartment of Hand and Peripheral Nerve Surgery, Royal North Shore Hospital, Sydney, AUS.
Michael J SymesDepartment of Orthopaedics and Trauma Surgery, Royal North Shore Hospital, Sydney, AUS.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Midshaft clavicle fractures are common orthopaedic injuries with no consensus on optimal management. Large language models (LLMs) such as ChatGPT (OpenAI, San Francisco, USA) present a novel tool for patient education and clinical decision-making. This study aimed to evaluate the accuracy and consistency of ChatGPT's responses to patient-focused and clinical decision-making questions regarding this injury. Methods ChatGPT-4o mini was prompted three times with 14 patient-focused and orthopaedic clinical decision-making questions. References were requested for each response. Response accuracy was graded as: (I) comprehensive; (II) correct but inadequate; (III) mixed with correct and incorrect information; or (IV) completely incorrect. Two consultant and two trainee orthopaedic surgeons evaluated the accuracy and consistency of responses. References provided by ChatGPT were evaluated for accuracy. Results All 42 responses were graded as (III), indicating a mix of correct and incorrect information, with 78.6% consistency across the responses. Of the 128 references provided, 0.8% were correct, 10.9% were incorrect, and 88.3% were fabricated. Only 3.1% of references accurately reflected the cited conclusions. Conclusion ChatGPT demonstrates limitations in accuracy and consistency when answering patient-focused queries or aiding in orthopaedic clinical decision-making for midshaft clavicle fractures. Caution is advised before integrating ChatGPT into clinical workflows for patients or orthopaedic clinicians.

Indexed as

artificial intelligencechatgptclavicle operative fixationmidshaft clavicle fracturepatient education

Identifiers

PMID40342470
PMCPMC12059606

What OpenQuestion holds

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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.