Healing Rates of Massive Rotator Cuff Tears by Allograft Reconstruction vs Maximal Repair - A Prospective Randomized Controlled Trial
Bibliographic record
Abstract
Objectives: To determine the healing rate of rotator cuff reconstruction with an acellular human dermal allograft compared with the current gold standard arthroscopic maximal rotator cuff repair of large, chronic tears of the rotator cuff. Methods: Thirty patients with a two-tendon chronic retracted rotator cuff tear were enrolled in the study and were randomly allocated (15) to each group. All the patients were evaluated for structural integrity of repair using a 1.5 T MRI at an average of 15 months after surgery. Rotator cuff arthropathy (RCA) and acromio-humeral distance (AHD) were graded using X-rays. WORC, DASH, MARX scores, range of motion (ROM) of shoulder were also analyzed. Results: The re-tear rate in reconstruction group was 13% (2 of 15 patients) compared to 67%(10/15) in repair group (p=0.008). Progression of RCA was seen in 7.14%(1/14) and 35.71%(4/14) of patients in reconstruction and repair group; respectively (p=0.006). The change in AHD (preop-postop) was significantly higher in repair (reduced by 2.27 mm) than the reconstruction group (increased by 0.1 mm) (P=0.006). Both groups had significant improvements in patient reported outcome scores. Reconstruction group had statistically significant better forward flexion (p= 0.01) and scapular plane abduction (p=0.03) compared to repair. Conclusion: Rotator cuff reconstruction with a dermal allograft demonstrated favourable structural healing rates and improved range of motion compared to maximal repair in the short term. Moreover, those in the maximal repair group were more likely to develop RCA compared to reconstruction.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".