Tough gel adhesive is an effective method for meniscal repair in a bovine cadaveric study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it