Evidence map›Paper›PMID 39330944›Full record

ArticleClinical orthopaedics and related research2025

Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?

Paul G Guirguis, Mark P Youssef, Ankit Punreddy, Mina Botros, Mattie Raiford, Susan McDowell

Abstract readComparative Study
In one paragraph

Article in Clinical orthopaedics and related research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
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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.

Paul G GuirguisUniversity of Rochester School of Medicine and Dentistry, Rochester, NY, USA.ORCID 0000-0001-6485-1822
Mark P YoussefA.T. Still School of Osteopathic Medicine, Mesa, AZ, USA.ORCID 0009-0005-6227-4941
Ankit PunreddyUniversity of Rochester School of Medicine and Dentistry, Rochester, NY, USA.ORCID 0009-0003-2202-7343
Mina BotrosDepartment of Orthopaedics and Physical Performance, University of Rochester Medical Center, Rochester, NY, USA.
Mattie RaifordDepartment of Orthopaedics and Physical Performance, University of Rochester Medical Center, Rochester, NY, USA.
Susan McDowellDepartment of Orthopaedics and Physical Performance, University of Rochester Medical Center, Rochester, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients and caregivers may experience immense distress when receiving the diagnosis of a primary musculoskeletal malignancy and subsequently turn to internet resources for more information. It is not clear whether these resources, including Google and ChatGPT, offer patients information that is readable, a measure of how easy text is to understand. Since many patients turn to Google and artificial intelligence resources for healthcare information, we thought it was important to ascertain whether the information they find is readable and easy to understand. The objective of this study was to compare readability of Google search results and ChatGPT answers to frequently asked questions and assess whether these sources meet NIH recommendations for readability. QUESTIONS/PURPOSES: (1) What is the readability of ChatGPT-3.5 as a source of patient information for the three most common primary bone malignancies compared with top online resources from Google search? (2) Do ChatGPT-3.5 responses and online resources meet NIH readability guidelines for patient education materials?

methodsThis was a cross-sectional analysis of the 12 most common online questions about osteosarcoma, chondrosarcoma, and Ewing sarcoma. To be consistent with other studies of similar design that utilized national society frequently asked questions lists, questions were selected from the American Cancer Society and categorized based on content, including diagnosis, treatment, and recovery and prognosis. Google was queried using all 36 questions, and top responses were recorded. Author types, such as hospital systems, national health organizations, or independent researchers, were recorded. ChatGPT-3.5 was provided each question in independent queries without further prompting. Responses were assessed with validated reading indices to determine readability by grade level. An independent t-test was performed with significance set at p < 0.05.

resultsGoogle (n = 36) and ChatGPT-3.5 (n = 36) answers were recorded, 12 for each of the three cancer types. Reading grade levels based on mean readability scores were 11.0 ± 2.9 and 16.1 ± 3.6, respectively. This corresponds to the eleventh grade reading level for Google and a fourth-year undergraduate student level for ChatGPT-3.5. Google answers were more readable across all individual indices, without differences in word count. No difference in readability was present across author type, question category, or cancer type. Of 72 total responses across both search modalities, none met NIH readability criteria at the sixth-grade level.

conclusionGoogle material was presented at a high school reading level, whereas ChatGPT-3.5 was at an undergraduate reading level. The readability of both resources was inadequate based on NIH recommendations. Improving readability is crucial for better patient understanding during cancer treatment. Physicians should assess patients' needs, offer them tailored materials, and guide them to reliable resources to prevent reliance on online information that is hard to understand. LEVEL OF EVIDENCE: Level III, prognostic study.

Indexed as

Bone NeoplasmsComprehensionConsumer Health InformationHealth LiteracyInternetOsteosarcomaPatient Education as TopicReadingSarcoma, EwingCross-Sectional StudiesHealth Knowledge, Attitudes, PracticeHumansLarge Language ModelsSearch EngineUnited States

Identifiers

PMID39330944
PMCPMC11753740

What OpenQuestion holds

Textmetadata
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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.