Machine learning–based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study
Jae-Yoon Baek, Eun-Ji Yoon, Jinyoung Jeong, Yang-Soo Kim, Hyun Seok Song, Hyo-Jin Lee, Hyungsuk Kim, Sung-Hyun Cho, Min Jeon, Do-Hyun Jung, Youn Kyu Lee, Tae Hyung Kim, Jong-Ho Kim · 2026
Read Machine learning–based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study by Jae-Yoon Baek, Eun-Ji Yoon, Jinyoung Jeong, Yang-Soo Kim, Hyun Seok Song, Hyo-Jin Lee, Hyungsuk Kim, Sung-Hyun Cho, Min Jeon, Do-Hyun Jung, Youn Kyu Lee, Tae Hyung Kim, Jong-Ho Kim (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13634079.txt.
Description
Jae-Yoon Baek and colleagues published a 2026 study predicting shoulder repair failure with machine learning. Rotator cuff tears are repaired through keyhole surgery. Some repairs tear again within months. Many factors influence that risk. The team gathered records from four hospitals.
More than fourteen hundred patients had complete data. Seventy-nine clinical variables were recorded for each patient. Follow-up scans at six months defined retear. Five learning models competed to predict it. Inputs included tear size, strength, and pain scores. Better prediction could personalize rehabilitation. High-risk patients might get closer monitoring or slower loading.
Catalog record
| CREATOR | Jae-Yoon Baek, Eun-Ji Yoon, Jinyoung Jeong, Yang-Soo Kim, Hyun Seok Song, Hyo-Jin Lee, Hyungsuk Kim, Sung-Hyun Cho, Min Jeon, Do-Hyun Jung, Youn Kyu Lee, Tae Hyung Kim, Jong-Ho Kim |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |