Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model
Omar Musbahi, Kyriacos Pouris, Savvas Hadjixenophontos, Ahmed Al-Saadawi, Iris Soteriou, Justin Peter Cobb, Gareth G Jones · 2025
Read Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model by Omar Musbahi, Kyriacos Pouris, Savvas Hadjixenophontos, Ahmed Al-Saadawi, Iris Soteriou, Justin Peter Cobb, Gareth G Jones (2025) 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-12400338.txt.
Description
Omar Musbahi and co-authors test, in a 2025 feasibility study, whether machine learning can predict which knee arthritis patients a steroid injection will actually help. The injections are widely used for pain but work unevenly. A tool that sorts likely responders from the rest would spare many a useless needle.
Data from two large prospective cohorts were combined. The outcome was patient-reported pain change on a standard scale, judged against thresholds for meaningful relief. An algorithm learned the patterns that precede a good response. Knee osteoarthritis care starts with pain control, and injections sit early in that ladder. Even a modest predictor could redirect non-responders sooner to options that fit them.
Catalog record
| CREATOR | Omar Musbahi, Kyriacos Pouris, Savvas Hadjixenophontos, Ahmed Al-Saadawi, Iris Soteriou, Justin Peter Cobb, Gareth G Jones |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |