Subscapularis management in stemless total shoulder arthroplasty: tenotomy versus peel versus lesser tuberosity osteotomy
Bibliographic record
Abstract
BACKGROUND: It is unknown whether subscapularis management technique has an influence on the outcomes and complications of stemless total shoulder arthroplasty. The purpose of this study, therefore, was to compare outcomes and complications between subscapularis tenotomy, peel, and lesser tuberosity osteotomy used during stemless shoulder arthroplasty. METHODS: We reviewed 188 stemless anatomic total shoulder arthroplasties and compared clinical and functional outcomes between those performed through a subscapularis tenotomy (n = 68), subscapularis peel (n = 65), or lesser tuberosity osteotomy (n = 55). Patients were followed up clinically and radiographically at 6 months, 1 year, and 2 years postoperatively. RESULTS: At 2 years postoperatively, no statistically significant differences in visual analog scale pain scores, American Shoulder and Elbow Surgeons scores, or patient-reported instability (P ≥ .19) were found between groups. Active external rotation was greater in the peel group (P = .006) than in the tenotomy group but was not different compared with the lesser tuberosity osteotomy group (P = .07). No statistically significant difference in clinical subscapularis failures was noted between groups (P = .11); however, 2 patients in the peel group sustained a subscapularis failure requiring reoperation. DISCUSSION: The results of this multicenter comparative analysis show that all 3 subscapularis management techniques are effective and safe in the short term when used with stemless 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".