Latissimus Dorsi Tendon Transfer Augmented by Human Dermal Tissue Allograft for Massive Rotator Cuff Tears: Surgical Technique
Bibliographic record
Abstract
Successful surgical treatment of irreparable massive rotator cuff tears is a major challenge in orthopedic surgery. For symptomatic active patients with rotator cuff lesions without underlying degenerative arthritis, a tendon transfer is a reasonable reconstructive treatment option. Since its description by Gerber in 1988, latissimus dorsi tendon transfer has become an established procedure but has had variable results especially in patients with associated subscapularis tendon tears. The lack of consistent healing of the latissimus dorsi tendon transfer due to the tenuous harvested tendon and recipient tissues may be a key cause of suboptimal clinical results. We describe a latissimus dorsi transfer procedure specifically augmented by an acellular dermal allograft, which serves as a reinforcing onlay on the bursal side of the transferred tendon. At the completion of the reconstruction, the host latissimus tendon is in direct contact with both the native prepared bone on the tuberosity footprint and the surrounding tissues. The goal of this procedure is not only to augment the native latissimus tendon and improve its tensile properties during the healing process, but also to achieve complete superior humeral head coverage with the potential for force couple restoration and increased function of a deficient subscapularis tendon.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".