Arthroscopic revision rotator cuff repair of large and massive retears using an interpositional bridging dermal allograft
Bibliographic record
Abstract
Background The purpose of this study was to report the clinical outcomes and retear rate following arthroscopic interpositional bridging dermal allograft for revision rotator cuff repair of large and massive retears. Methods Twenty-three patients were retrospectively reviewed at a minimum follow-up of 24 (mean, 47; range, 24-77) months after revision rotator cuff repair using an interpositional bridging dermal allograft. There were 17 males and 6 females with a mean age of 56 (range, 40-74) years. Clinical outcomes were assessed using range of motion, the American Shoulder and Elbow Surgeons score and Western Ontario Rotator Cuff Index. Graft integrity was assessed at 12-months using magnetic resonance imaging. Results The interval between the primary rotator cuff repair and interpositional bridging graft was a mean of 82 (range, 7-192) months. Forward flexion improved from a mean of 145° (range, 60-180°) preoperatively to 152° (range, 135-170°) postoperatively ( P = .3561). There was a decrease in external rotation from a mean of 50° (range, 20-80°) preoperatively to 37° (range, 0-45°) postoperatively ( P = .0021). The American Shoulder and Elbow Surgeons score improved ( P = .0196) from a mean of 50 (range, 10-88) to 69 (range, 22-97), and the Western Ontario Rotator Cuff index improved ( P = .0008) from a mean of 34 (range, 3-90) to 57 (range, 14-93). The graft was intact in 39% of patients. No patients underwent further surgery. Conclusion Interpositional bridging grafting for revision rotator cuff repair of large and massive retears leads to a significant improvement in functional outcome but is associated with a high retear rate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".