Evidence map›Paper›PMID 42668804›Full record

ArticleCureus2026

Evaluating the Citation Accuracy of ChatGPT-5 Using the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines.

Ethan M Bernstein, Brandon Bol, Jean Shanaa, Anya Ramsamooj, Tony Baldini, Zachary C Lum

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

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.

Ethan M BernsteinCollege of Medicine, California Northstate University, Elk Grove, USA.
Brandon BolCollege of Medicine, California Northstate University, Elk Grove, USA.
Jean ShanaaCollege of Medicine, California Northstate University, Elk Grove, USA.
Anya RamsamoojCollege of Medicine, California Northstate University, Elk Grove, USA.
Tony BaldiniCollege of Medicine, California Northstate University, Elk Grove, USA.
Zachary C LumOrthopedic Surgery, UC Davis School of Medicine, Sacramento, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge language models (LLMs) have demonstrated strong performance in generating medical responses; however, concerns persist regarding the accuracy of citations for these responses. Prior work has shown that earlier models frequently fabricate or misattribute references when queried on American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines (CPGs). With rapid advancements in model development, newer-generation LLMs may demonstrate improved citation accuracy and reliability. This study evaluates the citation accuracy of ChatGPT-5 in response to AAOS CPG-based queries.

methodsA computer-based observational study evaluating the citation accuracy of ChatGPT-5 (December 2025) was conducted from February to March 2026. Seventy recommendations from four AAOS CPGs were converted into standardized prompts, and ChatGPT was asked to generate a list of references to support its claims in response to these prompts. Five independent graders evaluated responses for citation accuracy. Citation elements assessed included title, authorship, journal, year, volume, pages, and PubMed identifier (PMID). Hallucinated references were defined as nonexistent or non-indexed studies.

resultsA total of 350 queries generated 2,736 references, of which 7.13% were fabricated. Citation inaccuracies were most frequent for PMID (37.28%), title (30.70%), and pages (27.49%). Overall, 49.34% of citations were bibliographically accurate, and complete accuracy within a query occurred in 8.00% of cases.

conclusionsCitation errors and fabricated references persist in ChatGPT-5. Independent verification of generated references therefore remains necessary.

Indexed as

artificial intelligencecitation accuracycitationsclinical practice guidelineslarge language models

Identifiers

PMID42668804
PMCPMC13525731

What OpenQuestion holds

Textmetadata
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.