Evidence map›Paper›PMID 38887661›Full record

ArticleJournal of experimental orthopaedics2024

ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery.

Janina Kaarre, Robert Feldt, Bálint Zsidai, Eric Hamrin Senorski, Emilia Möller Rydberg, Olof Wolf, Sebastian Mukka, Michael Möller, Kristian Samuelsson

Abstract read
In one paragraph

Article in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Janina KaarreDepartment of Orthopaedics, Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.ORCID 0000-0003-2559-8283
Robert FeldtDepartment of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden.
Bálint ZsidaiDepartment of Orthopaedics, Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.
Eric Hamrin SenorskiSahlgrenska Sports Medicine Center Gothenburg Sweden.
Emilia Möller RydbergDepartment of Orthopaedics, Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.
Olof WolfDepartment of Surgical Sciences, Orthopaedics Uppsala University Uppsala Sweden.
Sebastian MukkaDepartment of Surgical and Perioperative Sciences (Orthopaedics) Umeå University Umeå Sweden.
Michael MöllerDepartment of Orthopaedics, Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.
Kristian SamuelssonDepartment of Orthopaedics, Institute of Clinical Sciences Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To assess the possibility of using Generative Pretrained Transformer (ChatGPT) specifically in the context of orthopaedic trauma surgery by questions posed to ChatGPT and to evaluate responses (correctness, completeness and adaptiveness) by orthopaedic trauma surgeons. Methods: ChatGPT (GPT-4 of 12 May 2023) was asked to address 34 common orthopaedic trauma surgery-related questions and generate responses suited to three target groups: patient, nonorthopaedic medical doctor and expert orthopaedic surgeon. Three orthopaedic trauma surgeons independently assessed ChatGPT's responses by using a three-point response scale with a response range between 0 and 2, where a higher number indicates better performance (correctness, completeness and adaptiveness). Results: A total of 18 (52.9%) of all responses were assessed to be correct (2.0) for the patient target group, while 22 (64.7%) and 24 (70.5%) of the responses were determined to be correct for nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Moreover, a total of 18 (52.9%), 25 (73.5%) and 28 (82.4%) of the responses were assessed to be complete (2.0) for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. The average adaptiveness was 1.93, 1.95 and 1.97 for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Conclusion: The study results indicate that ChatGPT can yield valuable and overall correct responses in the context of orthopaedic trauma surgery across different target groups, which encompassed patients, nonorthopaedic medical surgeons and expert orthopaedic surgeons. The average correctness scores, completeness levels and adaptiveness values indicated the ability of ChatGPT to generate overall correct and complete responses adapted to the target group. Level of Evidence: Not applicable.

Indexed as

AIartificial intelligencelarge language modelsLLMs trauma orthopaedics

Identifiers

PMID38887661
PMCPMC11180970

What OpenQuestion holds

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LicenceCC BY
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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.