Surgical reconstruction techniques for irreparable subscapularis tears provide functional improvement but variable failure rates: A systematic review
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Irreparable subscapularis tears can cause severe functional impairment and present significant clinical challenges. Current treatment options include tendon transfers (TTs), anterior capsular reconstruction, and reverse shoulder arthroplasty. Each approach has distinct biomechanical advantages and limitations, but there remains no consensus regarding the optimal treatment. This systematic review aimed to evaluate and compare clinical outcomes, failure rates, and complication profiles of surgical reconstruction techniques for irreparable subscapularis tears. METHODS: This systematic review followed PRISMA guidelines. A comprehensive search was conducted in Embase, OVID Medline, and Emcare databases. Eligible studies included adults with irreparable subscapularis tears treated with surgical reconstruction (e.g., tendon transfers, graft augmentation, or anterior capsular reconstruction) and reporting clinical outcomes. Methodological quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) score. A narrative synthesis was performed with descriptive statistics (frequencies, percentages, or weighted means with variability). RESULTS: Fourteen studies comprising 351 patients (355 shoulders) were included, with a mean age of 58.1 years (SD 9.5) and mean follow-up of 44.7 months (SD 54.4). Studied procedures included latissimus dorsi (LD) TT (6 studies, n = 164), pectoralis major (PM) TT (5 studies, n = 94), pectoralis minor (Pm) TT (1 study, n = 74), and anterior capsular reconstruction (ACR; 2 studies, n = 25). PM TT had the highest failure rate (13.0 %), followed by ACR (12.0 %), LD transfer (11.0 %), and Pm TT (1.4 %). Postoperative complications were most frequent after PM TT (12.8 %), while LD TT had a complication rate of 9.8 %. Patient-reported outcome measures improved across all groups, with the greatest Constant-Murley Score (CMS) improvement following LD TT (+33.0), the greatest Subjective Shoulder Value (SSV) improvement after PM TT (+38.6), and the largest Visual Analogue Scale (VAS) pain reduction following ACR (-5.0) and Pm transfer (-5.1). CONCLUSIONS: Surgical reconstruction techniques for irreparable subscapularis tears provide improvements in pain and function, though failure and complication rates vary by procedure and appear worse with concomitant rotator cuff pathology. Further high-quality comparative studies are needed to refine patient selection and optimize surgical decision-making. LEVEL OF EVIDENCE: IV.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".