Can Grafts Provide Superior Tendon Healing and Clinical Outcomes After Rotator Cuff Repairs?
Bibliographic record
Abstract
Background: Arthroscopic repair of large to massive rotator cuff tears commonly retear. To improve healing rates, a number of different approaches have been utilized, including the use of grafts, which may enhance the biomechanical and biologic aspects of the repair construct. However, the outcomes after the use of grafts are diverse. Purpose: To systematically review the literature for large to massive rotator cuff tears to determine whether the use of grafts generally provides superior tendon healing and clinical outcomes to the repairs without grafts. Study Design: Systematic review; Level of evidence, 3. Methods: A systematic review of the literature was performed. Clinical studies comparing the repairs with (graft group) and without grafts (control group) were included and analyzed. The primary outcome was tendon healing on either magnetic resonance imaging or ultrasound. The secondary outcome measures included visual analog scale for pain, University of California at Los Angles (UCLA) score, and forward elevation range. Differences between groups in all outcome measures were statistically analyzed. Results: Six comparative studies (level of evidence 2 or 3) with 13 study groups were included. A total of 242 repairs in the graft group (mean age, 62.5 ± 4.6 years) and 185 repairs in the control group (mean age, 62.5 ± 5.0 years) were analyzed. The graft types utilized included autograft (fascia lata) in 1 study, allograft (human dermis) in 2 studies, xenograft (bovine pericardium, porcine small intestine submucosa) in 2 studies, synthetic graft (polypropylene) in 1 study, and a combination of autograft (the long head of biceps) and synthetic graft (polypropylene) in 1 study. The overall mean follow-up time was 28.4 ± 9.0 months. When 1 or 2 studies/study groups were excluded due to practical or statistical reasons, the graft group demonstrated significantly improved healing (odds ratio, 2.48; 95% CI, 1.58-3.90; P < .0001) and all clinical outcome measures at final follow-up ( P ≤ .02). Conclusion: The use of grafts generally provides superior tendon healing and clinical outcomes compared to repairs without grafts, except for some specific graft types (eg, porcine small intestine submucosa, bovine pericardium). Further investigations are required to determine the benefits of the use of grafts.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".