Graft Utilization in the Bridging Reconstruction of Irreparable Rotator Cuff Tears: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tears are one of the most common conditions affecting the shoulder. Because of the difficulty in managing massive rotator cuff tears and the inability of standard techniques to prevent arthropathy, surgeons have developed several novel techniques to improve outcomes and ideally alter the natural history. PURPOSE: To systematically review the existing literature and analyze reported outcomes to evaluate the effectiveness of using a bridging graft reconstruction technique to treat large to massive irreparable rotator cuff tears. STUDY DESIGN: Systematic review. METHODS: A systematic search of PubMed, EMBASE, CINAHL, and CENTRAL was employed with the key terms "tear," "allograft," and "rotator cuff." Eligibility was determined by a 3-phase screening process according to the outlined inclusion/exclusion criteria. Data in relation to the primary and secondary outcomes were summarized. The results were synthesized according to the origin of the graft and the level of evidence. RESULTS: Fifteen studies in total were included in this review: 2 comparative studies and 13 observational case series. Both the biceps tendon and the fascia lata autograft groups had significantly superior structural integrity rates on magnetic resonance imaging at 12-month minimum follow-up when compared with their partial primary repair counterparts (58% vs 26%, P = .036; 79% vs 58%, P < .05), respectively. Multiple noncomparative case series investigating allografts, xenografts, and synthetic materials for bridging reconstruction of large to massive rotator cuff tears demonstrated high structural healing rates (74%-90%, 73%-100%, and 60%-90%, respectively). Additionally, both comparative studies and case series demonstrated a general improvement of patients' functional outcome scores. CONCLUSION: Using a graft for an anatomic bridging rotator cuff repair results in improved function on objective testing and may be functionally better than nonanatomic or partial repair of large to massive rotator cuff tears. Allograft or xenograft techniques appear to be favorable options, given demonstrated functional improvement, imaging-supported graft survival, and lack of harvest complication risk. More high-quality randomized controlled studies are needed to further assess this technique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".