Evidence map›Paper›PMID 40980214›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2025

ChatGPT Provides Accurate but Incomplete Responses and Reliably Adjusts Readability to Prompts for Hamstring Injury Frequently Asked Questions.

Thomas W Fenn, Dominic M Farronato, Douglas K Wells, George B Reahl, F Winston Gwathmey, Charles A Su

Abstract read
In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

6 authors.

Thomas W FennDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.
Dominic M FarronatoDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.
Douglas K WellsDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.
George B ReahlDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.
F Winston GwathmeyDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.
Charles A SuDivision of Sports Medicine, Department of Orthopaedic Surgery, University of Virgina Health, Charlottesville, Virginia, U.S.A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the accuracy of ChatGPT's responses to frequently asked questions (FAQs) about hamstring injuries and to determine, if prompted, whether ChatGPT could appropriately tailor the reading level to that suggested. Methods: A preliminary list of 15 questions on hamstring injuries was developed from various FAQ sections on patient education websites from a variety of institutions, from which the 10 most frequently cited questions were selected. Three queries were performed, inputting the questions into ChatGPT-4.0: (1) unprompted, naïve, (2) additional prompt specifying the response being tailored to a seventh-grade reading level, and (3) additional prompt specifying the response being tailored to a college graduate reading level. The responses from the unprompted query were independently evaluated by two of the authors. To assess the quality of the answers, a grading system was applied: (A) correct and sufficient response; (B) correct but insufficient response; (C) response containing both correct and incorrect information; and (D) incorrect response. In addition, the readability of each response was measured using the Flesch-Kinkaid Reading Ease Score (FRES) and Grade Level (FKGL) scales. Results: Ten responses were evaluated. Inter-rater reliability was 0.6 regarding grading. Of the initial query, 2 of 10 responses received a grade of A, seven were graded as B, and one were graded as C. The average cumulative FRES and FKGL scores of the initial query was 61.64 and 10.28, respectively. The average cumulative FRES and FKGL scores of the secondary query were 75.2 and 6.1, respectively. Finally, the average FRES and FKGL scores of the third query were 12.08 and 17.23. Conclusions: ChatGPT showed generally satisfactory accuracy in responding to questions regarding hamstring injuries, although certain responses lacked completeness or specificity. On initial, unprompted queries, the readability of responses aligned with a tenth-grade level. However, when explicitly prompted, ChatGPT reliably adjusted the complexity of its responses to both a seventh-grade and a graduate-level reading standard. These findings suggest that although ChatGPT may not consistently deliver fully comprehensive medical information, it possesses the capacity to adapt its output to meet specific readability targets. Clinical Relevance: Artificial intelligence models like ChatGPT have the potential to serve as a supplemental educational tool for patients with orthopaedic to aid medical-decision making. It is important that we continually review the quality of they medical information generated by these artificial models as the evolve and improve.

Identifiers

PMID40980214
PMCPMC12447135

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.