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Articles Published Processes
12/11/2025 8:20:57 AM | Browse: 3 | Download: 12
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Received |
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2025-09-09 03:02 |
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Peer-Review Started |
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2025-09-09 03:02 |
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First Decision by Editorial Office Director |
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2025-10-11 03:07 |
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Return for Revision |
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2025-10-11 03:07 |
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Revised |
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2025-10-15 13:10 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-11-03 02:43 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-11-03 08:50 |
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Articles in Press |
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2025-11-03 08:50 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-12-02 01:49 |
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Publish the Manuscript Online |
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2025-12-11 08:20 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| 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 |
Surgery |
| Manuscript Type |
Letter to the Editor |
| Article Title |
Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Francisco Tustumi, Felipe Antonio Boff Maegawa and Pedro Luiz Serrano Uson Junior |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Francisco Tustumi, Center for Personalized Medicine, Hospital Israelita Albert Einstein, Avenue Albert Einstein, 627/701-Morumbi, São Paulo 05652900, Brazil. franciscotustumi@gmail.com |
| Key Words |
Bayes theorem; Artificial intelligence; Probability learning; Prediction algorithms; Risk |
| Core Tip |
Risk prediction in surgical oncology is evolving beyond traditional scoring systems. Frequentist methods, widely used in machine learning, offer transparency and reproducibility but rely solely on observed data. Bayesian reasoning, by contrast, integrates prior clinical knowledge with new information, mirroring real-world decision-making. The integration of both frameworks promises more reliable, interpretable, and personalized prediction tools, advancing the future of surgical care. |
| Publish Date |
2025-12-11 08:20 |
| Citation |
Tustumi F, Maegawa FAB, Serrano Uson Junior PL. Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms. World J Gastrointest Oncol 2025; 17(12): 113988 |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i12/113988.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i12.113988 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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