3D strain in native medial meniscus is comparable to medial meniscus allograft transplant
Bibliographic record
Abstract
PURPOSE: Injury or degeneration of the meniscus has been associated with the development of osteoarthritis of the knee joint. Meniscal allograft transplant (MAT) has been shown to reduce pain and restore function in patients who remain symptomatic following meniscectomy. The purpose of this study is to evaluate and compare the three-dimensional (3D) strain in native medial menisci compared to allograft-transplanted medial menisci in both the loaded and unloaded states. METHODS: Ten human cadaveric knees underwent medial MAT, utilizing soft-tissue anterior and posterior root fixation via transosseous sutures tied over an anterolateral proximal tibial cortical bone bridge. The joint was imaged first in the non-loaded state, then was positioned at 5° of flexion and loaded to 1× body weight (650 ± 160 N) during MR image acquisition. Anatomical landmarks were chosen from each image to create a tibial coordinate system, which were then input into a custom-written program (Matlab R2014a) to calculate the 3D strain from the unloaded and loaded marker positions. Six independent strains were obtained: three principal strains and three shearing strains. RESULTS: No statistically significant difference was found between the middle and posterior strains in the native knee compared to the meniscus allograft. This would suggest that soft-tissue fixation of meniscal allografts results in similar time zero principal and shear strains in comparison to the native meniscus. CONCLUSION: These results suggest that time zero MAT performs in a similar manner to the native meniscus. Optimizing MAT strain behavior may lead to potential improvements in its chondroprotective effect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".