Arthroscopic Repair of Articular Surface Partial-Thickness Rotator Cuff Tears: Transtendon Technique versus Repair after Completion of the Tear—A Meta-Analysis
Bibliographic record
Abstract
Articular surface partial-thickness rotator cuff tears (PTRCTs) are commonly repaired using two different surgical techniques: transtendon repair or repair after completion of the tear. Although a number of studies have demonstrated excellent clinical outcomes, it is unclear which technique may provide superior clinical outcomes and tendon healing. The purpose was to evaluate and compare the clinical outcomes following arthroscopic repair of articular surface PTRCT using a transtendon technique or completion of the tear. A systematic review of the literature was performed following PRISMA guidelines and checklist. The objective outcome measures evaluated in this study were the Constant Score, American Shoulder and Elbow Surgeons score, Visual Analogue Scale, physical examination, and complications. Three studies met our criteria. All were prospective randomized comparative studies with level II evidence and published from 2012 to 2013. A total of 182 shoulders (mean age 53.7 years; mean follow-up 40.5 months) were analyzed as part of this study. Both procedures provided excellent clinical outcomes with no significant difference in Constant Score and other measures between the procedures. Both procedures demonstrated improved clinical outcomes. However, there were no significant differences between each technique. Further studies are required to determine the long-term outcome of each technique.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".