Arthroscopic Repair of Medium to Large Rotator Cuff Tears With a Triple‐Loaded Medially Based Single‐Row Technique Augmented With Marrow Vents
Bibliographic record
Abstract
PURPOSE: The primary purpose of this study was to evaluate the repair integrity on magnetic resonance imaging (MRI), and secondarily, clinical outcomes, of medium to large (2-4 cm) rotator cuff tears treated using an arthroscopic triple-loaded medially based single-row repair technique augmented laterally with bone marrow vents. METHODS: This is a retrospective outcomes study of patients with full-thickness medium to large (2-4 cm) rotator cuff tears repaired by 4 surgeons at a single institution over a 2-year period with a minimum of 24 months' follow-up. A single-row repair with tension-minimizing medially based triple-loaded anchors and laterally placed bone marrow vents was used. Patients completed a satisfaction and pain survey, the Western Ontario Rotator Cuff index questionnaire, and a Short Form-36 version 2 survey to evaluate clinical outcomes. MRI was obtained at a minimum of 24 months follow-up to assess repair integrity. RESULTS: A total of 64 males and 27 females with a mean age of 59.7 (range, 34-82) were included. The mean tear size was 2.6 cm in anteroposterior dimension, treated with a mean of 2.2 anchors. Eighty-three of 91 shoulders (91%) reported being completely satisfied with their result. The median Western Ontario Rotator Cuff score was 95.2% of normal, with a significant difference found between those with an intact repair and those with a full-thickness recurrent defect (median, 95.9% vs. 73.8%; P = .003). Postoperative MRI obtained at a median of 32 months (range, 24-48) demonstrated an intact repair in 84 of 91 shoulders (92%), with failure defined as a full-thickness defect of the tendon. CONCLUSIONS: Arthroscopic repair of medium to large rotator cuff tears using triple-loaded medially based single-row repair augmented with marrow vents resulted in a 92% healing rate by MRI and excellent patient-reported outcomes LEVEL OF EVIDENCE: Level IV, retrospective case series.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".