Graft Augmentation Versus Bridging for Large to Massive Rotator Cuff Tears: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To systematically review the literature on the healing rates and clinical outcomes of the 2 different graft indications (i.e., augmentation vs bridging) during rotator cuff repair. METHODS: A systematic literature review was performed for clinical studies of rotator cuff repair using grafts for large to massive tears. The primary outcome was tendon healing on either magnetic resonance imaging or ultrasound. The secondary outcomes included visual analog scale for pain, American Shoulder and Elbow Surgeons score, and University of California at Los Angeles score, and forward elevation. Studies were divided into augmentation and bridging groups, and outcomes were compared statistically. RESULTS: Twelve studies with 13 study groups were included: 167 repairs in the augmentation group and 247 repairs in the bridging group. For augmentation and bridging groups, the mean age was 62.2 and 62.8 years and the mean follow-up was 28.5 and 37.7 months, respectively. The estimated healing rates were 64.0% for augmentation and 77.9% for bridging. Although both procedures had improved clinical outcomes, no statistical difference between groups was detected except lower visual analog scale in the bridging group at follow-up. CONCLUSIONS: Bridging grafts had no significant difference in healing or clinical outcomes when compared with a graft used for augmentation. Bridging grafts may be considered for this difficult patient population with large to massive rotator cuff tears. LEVEL OF EVIDENCE: Level IV, systematic review of Level II to IV studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".