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Articles Published Processes
6/17/2026 6:55:10 AM | Browse: 2 | Download: 8
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Received |
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2026-01-07 05:54 |
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Peer-Review Started |
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2026-01-07 05:55 |
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First Decision by Editorial Office Director |
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2026-01-23 08:00 |
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Return for Revision |
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2026-01-23 08:00 |
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Revised |
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2026-02-06 02:51 |
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Publication Fee Transferred |
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2026-02-10 21:35 |
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Second Decision by Editor |
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2026-03-30 02:34 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-03-30 08:35 |
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Articles in Press |
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2026-03-30 08:35 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-28 09:11 |
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Publish the Manuscript Online |
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2026-06-17 06:55 |
| ISSN |
2218-5836 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Orthopedics |
| Manuscript Type |
Observational Study |
| Article Title |
OpenEvidence performs at similar levels compared to current and previous GPT models on orthopedic training and education questions
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Kashif Javid, Alexander Driessche, Colton Clymer, Muhammad J Abbas, Annamarie Pantuso, Lindsay M Maier, Joseph Hoegler, William M Hakeos and Stuart T Guthrie |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Kashif Javid, Department of Orthopaedic Surgery, Henry Ford Health System, 2799 W. Grand Blvd, Detroit, MI 48202, Detroit, MI 48202, United States. kjavid1@hfhs.org |
| Key Words |
Artificial intelligence; Medical education; Orthopedic training; Large language models; Chatbot |
| Core Tip |
We evaluated the performance of contemporary large language models on orthopedic board-style questions, comparing ChatGPT-5 and Open Evidence (OE), with the established GPT-4. Using a standardized orthopedic training exam question set, we found that ChatGPT-5 achieved the highest overall accuracy and consistently outperformed prior models across subspecialties and question formats. OE performed comparably to GPT-4 across multiple fields. All models demonstrated reduced accuracy on image-based questions, highlighting persistent limitations in visual interpretation. We assert that OE is a reputable addition to the tools available to orthopedists. The added benefit of training drawn from peer-reviewed literature adds to its potential value. |
| Publish Date |
2026-06-17 06:55 |
| Citation |
Javid K, Driessche A, Clymer C, Abbas MJ, Pantuso A, Maier LM, Hoegler J, Hakeos WM, Guthrie ST. OpenEvidence performs at similar levels compared to current and previous GPT models on orthopedic training and education questions. World J Orthop 2026; 17(6): 118593
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| URL |
https://www.wjgnet.com/2218-5836/full/v17/i6/118593.htm |
| DOI |
https://doi.org/10.5312/wjo.v17.i6.118593 |
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