Lower trapezius tendon transfer for irreparable rotator cuff injuries: a scoping review
Bibliographic record
Abstract
Background: Rotator cuff tears are a common source of shoulder pain and dysfunction. An irreparable rotator cuff tear poses a particular treatment challenge. There have been few studies reporting the outcomes of lower trapezius tendon (LTT) transfer for irreparable rotator cuff injuries. Therefore, the purpose of this review is to summarize the postoperative functional outcomes and complications of patients undergoing a LTT transfer for massive irreparable rotator cuff injuries. Methods: A scoping review was performed using the Medline, Embase, Cochrane Central Register of Controlled Trials, and Google Scholar databases with the search terms "trapezius" AND "transfer." Of 362 studies included for initial screening, 37 full-text citations were reviewed, with 5 studies meeting all the inclusion criteria to be included in the review. Two reviewers extracted data on study design, patient demographics, surgical technique, functional outcomes, range of motion (ROM), and complications for each study according to the predefined criteria. Results: Improvements in the preoperative to postoperative functional status, identified using the Disabilities of the Arm, Shoulder, and Hand (50.34 to 18), The American Shoulder and Elbow Surgeons Score (48.56 to 80.24), Visual Analog Scale (5.8 to 1.89), Single Assessment Numeric Evaluation (34.22 to 69.86), and Subjective Shoulder Value (52.24 to 77.66), were evident across all 5 studies. Preoperative to postoperative increases in ROM were seen for flexion (85 to 135), external rotation (18 to 52), and abduction (50 to 98). The overall complication rate was 18%, with seroma formation (8%) as the most common postoperative complication. Discussion/Conclusion: Our analysis showed that LTT transfer improved postoperative function, ROM, and pain for patients with irreparable rotator cuff tears with an overall complication rate of 18%. Future controlled studies are required to directly compare LTT transfer to other tendon transfers and other surgical techniques for irreparable rotator cuff tears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".