Subscapularis management in anatomic total shoulder arthroplasty: A systematic review and network meta-analysis
Bibliographic record
Abstract
Background: This systematic review and network meta-analysis compare clinical outcomes of three different subscapularis management techniques in anatomic total shoulder arthroplasty: lesser tuberosity osteotomy, subscapularis peel, and subscapularis tenotomy. Methods: PubMed, Web of Science, Embase, and Cochrane's trial registry were searched in July 2021. Comparative studies and case series evaluating the outcomes of these three techniques were included. The network meta-analysis was performed only on comparative studies. Results: Twenty-three studies were included. Both lesser tuberosity osteotomy and subscapularis peel had significantly higher Western Ontario Osteoarthritis Scores compared to subscapularis tenotomy, but no difference in American Shoulder and Elbow Society Scores. Subscapularis peel had superior external rotation compared to lesser tuberosity osteotomy. However, no difference was found in external rotation between subscapularis peel and subscapularis tenotomy or between subscapularis tenotomy and lesser tuberosity osteotomy. The overall weighted average for lesser tuberosity osteotomy bony union was 93.6%, whereas the overall weighted average for subscapularis tendon healing was 79.4% and 87% for subscapularis tenotomy and subscapularis peel, respectively. Discussion: This network meta-analysis demonstrated that lesser tuberosity osteotomy and subscapularis peel were associated with the high union and subscapularis healing rates and may be associated with improved shoulder function and quality of life, compared to subscapularis tenotomy. Lesser tuberosity osteotomy and subscapularis peel demonstrate a trend of superior outcomes compared to subscapularis tenotomy during anatomic total shoulder arthroplasty.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".