Evidence map›Paper›PMID 40969547›Full record

ArticleOTA international : the open access journal of orthopaedic trauma2025

Improving the readability of trauma patient education materials: a ChatGPT solution demonstrated using materials by the Orthopaedic Trauma Association.

Oscar Covarrubias, Diane Ghanem, Christopher Murdock, Christopher Domes, Babar Shafiq

Abstract read
In one paragraph

Article in OTA international : the open access journal of orthopaedic trauma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Oscar CovarrubiasSchool of Medicine, The Johns Hopkins University, Baltimore, MD.ORCID https://orcid.org/0000-0001-6382-0944
Diane GhanemDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD.ORCID https://orcid.org/0000-0002-5894-3373
Christopher MurdockDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD.
Christopher DomesDepartment of Orthopedics and Rehabilitation, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Babar ShafiqDepartment of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: ChatGPT is an artificial intelligence language model capable of understanding, contextualizing, and generating human-like text. The purpose of this study was to assess the ability of ChatGPT to rewrite orthopaedic trauma patient education materials at the recommended sixth grade level. Methods: The academic grade level of each of the 41 Orthopaedic Trauma Association (OTA/AO) online patient education articles was evaluated using the Flesh-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE). Each article was then provided to ChatGPT along with instructions to simplify the readability of the text to a sixth grade level. The FKGL and FRE of the ChatGPT revised articles were calculated and compared with the original articles. Two orthopaedic trauma surgeons assessed the content of the revised articles and categorized them as "accurate," "refinable," or "insufficient" based on the preservation of information from the original articles. Results: ChatGPT significantly reduced the FKGL (8.2 ± 1.1‒5.7 ± 0.5, Conclusion: ChatGPT can be used to simplify and enhance the readability of patient education materials. The average readability of the OTA/AO educational articles was changed from an eighth grade to a fifth grade level. However, nearly a third of the ChatGPT revised articles required revisions due to content omissions thus highlighting the importance of expert review. Level of Evidence: NA.

Indexed as

artificial intelligenceChatGPTlarge language modelspatient educationreadabilitytrauma education

Identifiers

PMID40969547
PMCPMC12443154

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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.