Suture Tape Reduces Quadriceps Tendon Repair Gap Formation Compared With High‐Strength Suture: A Cadaveric Biomechanical Analysis
Bibliographic record
Abstract
PURPOSE: To compare the biomechanical differences between quadriceps tendon (QT) repair with high-strength suture (HSS) versus suture tape (ST) with varying number of suture passes. METHODS: In total, 28 fresh-frozen QTs were randomized into 2 groups: (1) HSS; or (2) ST; specimens were then further randomized into subgroups of either 4 or 6 suture passes. Specimens were secured within a materials testing system and a 150-N preload was applied for 10 seconds followed by a cyclic loading protocol between 50 N and 250 N for 1000 cycles. Video was used to follow tracking markers used to calculate the magnitude of tendon displacement. Two-way univariate analysis of variance was used to determine the effect of suture type and passes on the displacement after preloading and mixed repeated-measures analysis of variance was used to determine the effect of suture type and passes on displacement following cyclic loading. RESULTS: There were large increases in displacement following the preload across all conditions (7.82 ± 3.64 mm), with no statistically significant differences between groups. There was a significant difference in the mean (± standard deviation) displacement between the ST (5.24 ± 2.82 mm) and HSS (7.93 ± 2.91 mm) starting at 200 cycles, which became more pronounced with successive testing out to 1000 cycles (P = .021). There were no significant difference with respect to the number of suture or tape passes. CONCLUSIONS: Following preloading at 150 N, significant displacement occurred in both QT repair groups. ST demonstrated significantly less displacement than HSS under cyclic loading and had greater ultimate failure loads. CLINICAL RELEVANCE: When performing QT repair, emphasis should be placed on appropriate pretensioning of sutures to at least 150 N before knot-tying. In addition, where available, ST should be used over HSS to reduce further cyclic elongation and improve ultimate failure loads.
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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 | 0.001 |
| 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) | 0.003 | 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".