Evaluating tendon transfers in irreparable rotator cuff tears: A systematic review of clinical outcomes and failure rates
Bibliographic record
Abstract
Background: Irreparable rotator cuff tears (IRCTs) pose a clinical challenge, particularly in younger patients. Tendon transfers (TTs) have emerged as a joint-preserving surgical option. However, the clinical outcomes, failure rates, and complication profiles associated with different TTs remain incompletely defined. This systematic review evaluates outcomes across various TT techniques used to manage IRCTs. Methods: A systematic search of MEDLINE, Embase, and Emcare databases was conducted for studies involving adults with diagnosed IRCTs reporting patient-reported outcomes and failure rates following TT. Non-English studies, conference abstracts, case reports, and studies with <12 months follow-up were excluded. Descriptive summaries and pooled analyses were performed by tendon type. Results: Thirty studies (980 patients, 994 shoulders) were included. The mean patient age was 58.9 years, with a mean follow-up of 44.7 months. Latissimus dorsi (LD) transfers were most commonly reported. All tendon types showed significant improvements in patient-reported outcomes. Teres major transfers exhibited the highest Constant-Murley Score improvement (+40) and greatest pain reduction (VAS-5.6), although based on a single study. Failure rates ranged from 4.2% to 14.1%. Conclusions: TTs improve pain and function in IRCTs. LD transfers remain the most widely used. Variability in techniques and outcomes highlights the need for standardized protocols and further high-quality research.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".