Trials / Completed
CompletedNCT06145815
Machine Learning Predictive Model for Rotator Cuff Repair Failure
Predictive Model for Minimal Important Change After Rotator Cuff Repair Using Machine Learning Methods: A Pilot Study
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 4,789 (actual)
- Sponsor
- La Tour Hospital · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- —
Summary
There is little overall evidence behind clinical practice guidelines for diagnosis and treatment of rotator cuff repair. The purpose of this study was to compare the performance of different machine learning models that use pre-operative data from an international and multicentric database to predict if a patient that underwent rotator cuff repair could achieve the minimal important change (MIC) for single assessment numeric evaluation (SANE) at one year follow-up.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| PROCEDURE | Arthroscopic rotator cuff repair | Patients underwent an arthroscopic repair for rotator cuff lesions |
Timeline
- Start date
- 2022-09-01
- Primary completion
- 2022-09-01
- Completion
- 2023-11-01
- First posted
- 2023-11-24
- Last updated
- 2023-11-29
Locations
1 site across 1 country: Switzerland
Source: ClinicalTrials.gov record NCT06145815. Inclusion in this directory is not an endorsement.