Tough gel adhesive is an effective method for meniscal repair in a bovine cadaveric study
Bibliographic record
Abstract
PURPOSE: To test tough gel adhesives to repair meniscus tears under relevant loading conditions and determine if they have adequate biomechanical properties to repair meniscus tears in a bovine cadaveric study. METHODS: Cyclic compression tests on 24 dissected bovine knees were performed. The tough gel adhesive was used either as an adhesive patch or as a coating bonded onto commercially available surgical sutures. Forty-eight menisci were tested in this study; 24 complete radial tears and 24 bucket-handle tears. After preconditioning, the specimens underwent 100 cycles of compression, (800 N/0.5 Hz) on an Instron© machine and the size of the gaps measured. One third of the menisci were repaired with pristine sutures, one third with adhesive patches, and one third with sutures coated in adhesive gel. The size of the gaps was compared after 100 and 500 cycles of compression. RESULTS: The mean gap measured at the tear site without treatment was 6.46 mm (± 1.41 mm) for radial tears and 1.92 mm (± 0.65 mm) for bucket-handle tears. After treatment and 500 cycles of compression, the mean gap was 1.63 mm (± 1.41 mm) for pristine sutures, 1.50 mm (± 1.16 mm) for adhesive sutures and 2.06 mm (± 1.53 mm) for adhesive gel patches. There was no significant difference between treatments regardless of the type of tear. Also, the gaps for radial tears increased significantly with the number of compression cycles applied (p > 0.001). CONCLUSION: From a biomechanical standpoint, the tough adhesive gel patch is as effective as suturing. In addition, it would allow the repair of non-suturable tears and thus broaden the indications for meniscus repair. 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".