ArticleJSES international2025
Evaluation of the impact of large language learning models on publications in the Journal of Shoulder and Elbow Surgery.
Article in JSES international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Evolution of Artificial Intelligence Adoption inOrthopaedic journal of sports medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There has been growing interest in the application of artificial intelligence (AI) within academic research to enhance writing, data interpretation, and other tasks. However, it also raises concerns about plagiarism and fraudulent content. While AI-detection tools are available, no systematic review has examined AI use in shoulder and elbow surgery. This study evaluates AI utilization in Methods: We analyzed 232 publications in JSES, from January to April in both 2022 and 2024. Abstracts and full-length texts were manually entered and analyzed using ZeroGPT, an AI-content detector, and ChatGPT detector. A secondary analysis was performed on publications with suspected AI use of greater than 10%, 20%, 30%, 40%, and 50%. Variables analyzed were year of publication, country of origin, and probability of AI use. Univariate analyses according to geographic region were conducted on publications with a suspected AI percentage > 10% and > 20%. Results: A total of 232 publications were identified, 114 from January to April 2022 (pre-ChatGPT) and 118 from January to April 2024 (post-ChatGPT). The average percentage AI generation was 26% ± 18% within the abstracts and 5% ± 3% within the full-body text of included publications. While there was no significant difference in the suspected use of AI within the full texts before and after the launch of ChatGPT (4.7% ± 3.3% in 2022 vs. 5.3% ± 3.6% in 2024; Conclusion: This study highlights increased use of AI within the writing of JSES publications since the launch of ChatGPT-3.5. While the integration of AI introduces new opportunities in scientific research, there are ethical and methodological challenges that must be carefully considered.
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Registered trials
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.