Suture Augmentation of a Four‐Strand Semitendinosus Graft Improves Time‐Zero Biomechanical Properties
Bibliographic record
Abstract
PURPOSE: To compare the time-zero biomechanical properties of hamstring graft preparations with or without suture augmentation for anterior cruciate ligament reconstruction (ACLR) in a full-construct cadaveric model. METHODS: Hamstring grafts were harvested from 24 fresh frozen human cadavers and prepared in 1 of 3 ways: quadrupled SemiTendinosus (SemiT), and quadrupled SemiT with suture augmentation (SemiT+2.0-mm tape or SemiT+1.3-mm tape; n = 8 per group). Adjustable loop suspensory implants and cortical buttons were used for fixation on a porcine tibia and acrylic block. Testing included force-controlled cyclic loading at 250 N and 400 N followed by load to failure. RESULTS: The 2 suture augmentation groups had less total elongation and increased stiffness compared to the nonsuture-augmented group (P = .025). The SemiT+2.0-mm tape group had 36% less total elongation and 34% increased stiffness compared to SemiT+1.3mm tape (P < .001). CONCLUSIONS: Suture augmentation improves construct biomechanics at time zero following hamstring tendon ACLR. Augmentation with 2.0-mm tape suture improves construct biomechanics compared to 1.3-mm tape suture. CLINICAL RELEVANCE: Independent suture augmentation of a quadrupled SemiT graft improves ACLR construct biomechanics. Outcomes were improved with augmentation using 2.0-mm tape suture compared to 1.3-mm tape suture.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".