Enhancing meniscal repair with tough adhesive puncture sealing (TAPS) suture: A proof‐of‐concept study on bovine cadaveric knees
Bibliographic record
Abstract
Purpose: The objective was to use cyclic tensile loading to compare the gap formation at suture site of three different suture materials to repair bovine radial meniscal tears: (1) polyglactin sutures, (2) tough adhesive puncture sealing (TAPS) sutures and (3) ultra-high molecular weight polyethylene (UHMWPE) sutures. Methods: Twelve ex vivo bovine knees were dissected to retrieve the menisci. Complete radial tears were performed on 24 menisci, which were then separated into three groups and repaired using either pristine 2-0 polyglactin sutures, TAPS sutures (2-0 polyglactin sutures coated with adhesive chitosan/alginate hydrogels) or 2-0 UHMWPE sutures with a single stitch. The repaired menisci were clamped onto an Instron machine and underwent cyclic loading between 5 and 25 N at a frequency of 0.16 Hz. Gap formation between the edges of the tear was measured after 500 cycles using an electronic caliper, when the meniscus was still on the Instron without any load applied. Results: Mean gap formation was 5.22 mm (±1.70) for the 2-0 polyglactin sutures, 2.48 mm (±0.25) for the TAPS sutures, and 4.85 mm (±1.55) for the 2-0 UHMWPE sutures. The gap was significantly smaller in the TAPS sutures group compared to the two others because of better force dispersion, decreasing tissue damage by suture indentation and potentially leading to better meniscal healing. Conclusions: From a biomechanical standpoint, coated sutures held the edges of radial meniscal tears closer together compared to conventional sutures. This technology has the potential to reduce tissue damage and improve the success rate of meniscal repairs. Level of Evidence: controlled laboratory study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".