Frictional Properties of the Meniscus Improve After Scaffold-augmented Repair of Partial Meniscectomy: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: To prevent further degeneration, it is desirable to fill a meniscal defect with a supportive scaffold that mimics the mechanics of native tissue. Degradable porous scaffolds have been used, but it is unclear whether the tissue that fills the site of implantation is mechanically adequate, particularly with respect to frictional performance. QUESTIONS/PURPOSES: We therefore determined the frictional behavior of native and engineered meniscal replacement tissue from in vivo implantation over time. METHODS: We evaluated boundary and mixed-mode friction coefficients of tissue generated in porous polyurethane scaffolds used to augment the repair of the meniscus of 13 skeletally mature sheep after partial meniscectomy. Implants were removed for evaluation at 3, 6, and 12 months. The friction coefficient, aggregate modulus, and hydraulic permeability were evaluated for tissue harvested from native meniscus adjacent to the implants, native meniscus from the intact contralateral knee, and repair tissue from the site of the scaffold implantation. The equilibrium friction coefficient (μ(eq)) was measured in the presence of a lubricant bath of either phosphate-buffered saline (PBS) or equine synovial fluid (ESF). RESULTS: Boundary μ(eq) in PBS of engineered meniscus improved with time and was similar to native tissue after 6 months. ESF enhanced lubrication for all samples at nearly all time points demonstrating the efficacy of ESF as a joint lubricant for repair tissue as well as native meniscus. Modulus increased and permeability decreased with implantation, likely as a result of tissue ingrowth. CONCLUSIONS: Promoting tissue ingrowth into porous scaffolds is a potential strategy for improving friction performance in meniscal repair.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".