Full‐Thickness Rotator Cuff Tears Can Be Safely Treated With a Resorbable Bioinductive Bovine Collagen Implant: One‐Year Results of a Prospective, Multicenter Registry
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to prospectively collect safety and efficacy data in a large group of patients undergoing arthroscopic repair of full-thickness rotator cuff tears augmented with a resorbable bioinductive bovine collagen implant designed to promote healing. METHODS: Seventeen centers across the United States enrolled patients in an institutional review board-approved registry to collect outcomes data on the implant. Patients undergoing surgical management of full-thickness rotator cuff tears augmented with the implant were enrolled. Inclusion criteria were age of ≥21 years, willingness to participate and the ability to read and speak English. Exclusion criteria included hypersensitivity to bovine-derived products. Patients were assessed before and after surgery at up to 1 year with outcomes including the single-assessment numeric evaluation (SANE), Veterans RAND 12-Item (VR-12) mental components and physical components (VR-12 PCS), American Shoulder and Elbow Surgeons (ASES), and Western Ontario Rotator Cuff (WORC) outcome measures. Ad hoc analyses were performed to compare these outcomes at all time points depending on tear size (small/medium vs large/massive). Serious complications were collected. RESULTS: < .001 for all results). Ad-hoc analysis demonstrated that similar results were obtained at 1 year regardless of tear size. Twenty patients (10.4%) experienced serious complications (10.4%), including revision surgery (n = 18), proximal humerus fracture/partial subscapularis tear resulting from multiple falls (n = 1), and adhesive capsulitis (n = 1). CONCLUSIONS: The safety and efficacy of a bioinductive implant in the surgical management of full-thickness rotator cuff tears at 1 year was shown in this study. Implant efficacy appears to be comparable regardless of the underlying tear size. LEVEL OF EVIDENCE: Level IV, therapeutic case series.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".