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Interobserver Agreement in the Classification of Partial-Thickness Rotator Cuff Tears Using the Snyder Classification
Christopher S Lee1, Shane M Davis1, Brittany Doremus1
1Stetson Powell Orthopedics and Sports Medicine, Burbank, California, USA.
Orthopaedic Journal of Sports Medicine
|October 15, 2016
Summary
The Snyder classification system demonstrates moderate reliability and very good accuracy for classifying partial rotator cuff tears. This reproducible system can aid future research into rotator cuff tear treatment options.
Area of Science:
- Orthopaedic Surgery
- Musculoskeletal Imaging
- Surgical Classification Systems
Background:
- Partial-thickness rotator cuff tears lack a universally accepted classification system.
- This absence leads to controversy regarding optimal treatment strategies.
Purpose of the Study:
- To assess the interobserver reliability and accuracy of the Snyder classification system for partial rotator cuff tears.
- To determine if the Snyder classification is a reproducible method for categorizing these injuries.
Main Methods:
- A cohort of 27 orthopaedic surgeons reviewed 10 arthroscopic videos of shoulder surgeries.
- Surgeons utilized the Snyder classification system to categorize partial rotator cuff tears.
- Interrater reliability was measured using kappa statistics and percentage agreement; surgeon experience was also analyzed.
Main Results:
- The Snyder classification achieved a kappa coefficient of 0.512, indicating moderate interobserver reliability.
- The mean accuracy rate was 80%, signifying "very good" agreement among surgeons.
- No significant correlation was found between surgeon experience/fellowship training and classification accuracy.
Conclusions:
- The Snyder classification system is a reproducible tool for categorizing partial rotator cuff tears.
- This system holds potential for future research in evaluating treatment options for rotator cuff injuries.
Keywords:
Snyder classificationarthroscopyarticular surfacebursal surfacepartial rotator cuff tearssupraspinatus
