Latissimus dorsi tendon transfer in reverse shoulder arthroplasty: transfer location affects strength
Bibliographic record
Abstract
Background The optimal insertion location of a latissimus dorsi tendon transfer to restore external rotation after reverse shoulder arthroplasty (RSA) is not well established. The aim of this biomechanical study was to determine the effect of tendon transfer location on external rotation torque, in conjunction with varying RSA humeral component lateralization. We hypothesized that proximal tendon transfers, along with increasing humeral lateralization, would maximize external rotation torque. Methods Eight fresh-frozen cadaveric shoulders underwent RSA and were tested on an in vitro shoulder simulator. A latissimus dorsi tendon transfer was tested at three insertion locations (lateral greater tuberosity [Lat-GT]; teres minor footprint [Tm-FP]; lateral shaft [Lat-Shft]), and external rotation torque was measured. Additional test conditions included varying humeral component lateralization (-5, 0, +5, +10, +15 mm), abduction angle (0°, 45°, 90°), and internal/external rotation (-60°, -30°, 0°, 30°, 60°). Results The Lat-GT and Tm-FP insertions of the latissimus dorsi transfer both generated significantly greater torques ( P < .001) than the Lat-Shft. When comparing Lat-GT to Tm-FP, there were no significant differences ( P = .362). At 60˚ of external rotation, RSA humeral component lateralization from -5 to +15 mm significantly increased the external rotation torque of Lat-GT by 67% ( P = .035), Tm-FP by 43% ( P = .001), and of Lat-Shft by 42% ( P = .002). Conclusion Latissimus dorsi tendon transfer to the proximal lateral aspect of the greater tuberosity and to the insertion site of the teres minor generated significantly more external rotation torque than transfer to the lateral humeral shaft. In addition, the use of a humeral component with greater offset also substantially increases the torque generated by the tendon transfer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".