Evidence map›Paper›PMID 39238880›Full record

ReviewJB & JS open access

ChatGPT-4 Knows Its A B C D E but Cannot Cite Its Source.

Diane Ghanem, Alexander R Zhu, Whitney Kagabo, Greg Osgood, Babar Shafiq

Abstract readReview
In one paragraph

Review in JB & JS open access. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Reference Accuracy in Large Language Model Chatbots: A Metric for Inherent Misinformation?Medical science monitor : international medical journal of experimental and clinical research · 2026
    Article
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  5. Review
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

5 authors.

Diane GhanemDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, Maryland.ORCID https://orcid.org/0000-0002-5894-3373
Alexander R ZhuSchool of Medicine, The Johns Hopkins University, Baltimore, Maryland.ORCID https://orcid.org/0000-0002-3476-1065
Whitney KagaboDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, Maryland.ORCID https://orcid.org/0000-0003-4790-232X
Greg OsgoodDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, Maryland.ORCID https://orcid.org/0000-0001-6271-4971
Babar ShafiqDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, Maryland.ORCID https://orcid.org/0000-0002-9204-0477

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The artificial intelligence language model Chat Generative Pretrained Transformer (ChatGPT) has shown potential as a reliable and accessible educational resource in orthopaedic surgery. Yet, the accuracy of the references behind the provided information remains elusive, which poses a concern for maintaining the integrity of medical content. This study aims to examine the accuracy of the references provided by ChatGPT-4 concerning the Airway, Breathing, Circulation, Disability, Exposure (ABCDE) approach in trauma surgery. Methods: Two independent reviewers critically assessed 30 ChatGPT-4-generated references supporting the well-established ABCDE approach to trauma protocol, grading them as 0 (nonexistent), 1 (inaccurate), or 2 (accurate). All discrepancies between the ChatGPT-4 and PubMed references were carefully reviewed and bolded. Cohen's Kappa coefficient was used to examine the agreement of the accuracy scores of the ChatGPT-4-generated references between reviewers. Descriptive statistics were used to summarize the mean reference accuracy scores. To compare the variance of the means across the 5 categories, one-way analysis of variance was used. Results: ChatGPT-4 had an average reference accuracy score of 66.7%. Of the 30 references, only 43.3% were accurate and deemed "true" while 56.7% were categorized as "false" (43.3% inaccurate and 13.3% nonexistent). The accuracy was consistent across the 5 trauma protocol categories, with no significant statistical difference (p = 0.437). Discussion: With 57% of references being inaccurate or nonexistent, ChatGPT-4 has fallen short in providing reliable and reproducible references-a concerning finding for the safety of using ChatGPT-4 for professional medical decision making without thorough verification. Only if used cautiously, with cross-referencing, can this language model act as an adjunct learning tool that can enhance comprehensiveness as well as knowledge rehearsal and manipulation.

Identifiers

PMID39238880
PMCPMC11368215

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.