The Effect of Six Knotting Methods on the Biomechanical Properties of Three Large Diameter Absorbable Suture Materials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of 6 different knotting methods on the mechanical properties of 3 large absorbable suture materials used in large animal surgery. STUDY DESIGN: In vitro mechanical study. Sample Population- Knotted suture loops (n=15 per group). METHODS: Suture loops were created between two low-friction pulleys with either 2 polydioxanone, 2 polyglactin 910 or 3 polyglactin 910. Strands were tied using 1 of 6 knotting technique: square knot, surgeon knot, clamped surgeon's knot, sliding half-hitch knot (HH), Delimar knot and self-locking knot (SLK). A single cycle to failure test was performed on each suture loop with a distraction rate of 100 mm/min. Failure modes were evaluated and breaking strength, elongation to failure and stiffness were compared. RESULTS: All loops except two HH failed at the knot by acute breaking. The double-stranded SLK was both stronger and stiffer than all other knots for each suture material. Clamping the first throw of the surgeon knot decreased load to failure significantly (143.11 +/- 8.64 N) compared with not clamping (159.21 +/- 6.14 N) for polydioxanone. Stiffness and elongation to failure were respectively lower and increased for 2 polydioxanone compared with both polyglactin 910 materials for all knotting techniques. CONCLUSIONS: Knotting techniques do influence structural properties of suture loops. The double strand loop conferred stiffer and stronger properties to the SLK CLINICAL RELEVANCE: Clamping the first throw of polydioxanone should be avoided when tying a suture under tension even using large diameter suture materials. Using a SLK might be considered as a useful alternative when excessive tension is present.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".