Evidence map›Paper›PMID 38312282›Full record

ArticleJSES international2024

Evaluation of information from artificial intelligence on rotator cuff repair surgery.

Eric Warren, Eoghan T Hurley, Caroline N Park, Bryan S Crook, Samuel Lorentz, Jay M Levin, Oke Anakwenze, Peter B MacDonald, Christopher S Klifto

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JSES international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Evaluating if ChatGPT Can Answer Common Patient Questions Compared With OrthoInfo Regarding Rotator Cuff Tears.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

9 authors.

Eric WarrenDuke University School of Medicine, Duke University, Durham, NC, USA.
Eoghan T HurleyDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Caroline N ParkDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Bryan S CrookDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Samuel LorentzDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Jay M LevinDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Oke AnakwenzeDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Peter B MacDonaldSection of Orthopaedic Surgery & The Pan Am Clinic, University of Manitoba, Winnipeg, MB, Canada.
Christopher S KliftoDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to analyze the quality and readability of information regarding rotator cuff repair surgery available using an online AI software. Methods: An open AI model (ChatGPT) was used to answer 24 commonly asked questions from patients on rotator cuff repair. Questions were stratified into one of three categories based on the Rothwell classification system: fact, policy, or value. The answers for each category were evaluated for reliability, quality and readability using The Journal of the American Medical Association Benchmark criteria, DISCERN score, Flesch-Kincaid Reading Ease Score and Grade Level. Results: The Journal of the American Medical Association Benchmark criteria score for all three categories was 0, which is the lowest score indicating no reliable resources cited. The DISCERN score was 51 for fact, 53 for policy, and 55 for value questions, all of which are considered good scores. Across question categories, the reliability portion of the DISCERN score was low, due to a lack of resources. The Flesch-Kincaid Reading Ease Score (and Flesch-Kincaid Grade Level) was 48.3 (10.3) for the fact class, 42.0 (10.9) for the policy class, and 38.4 (11.6) for the value class. Conclusion: The quality of information provided by the open AI chat system was generally high across all question types but had significant shortcomings in reliability due to the absence of source material citations. The DISCERN scores of the AI generated responses matched or exceeded previously published results of studies evaluating the quality of online information about rotator cuff repairs. The responses were U.S. 10

Indexed as

Artificial intelligenceChatGPTNatural language processingPatient informationRotator cuff repairShoulder

Identifiers

PMID38312282
PMCPMC10837709

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.