New understanding of the complex structure of knee menisci: Implications for injury risk and repair potential for athletes
Bibliographic record
Abstract
Menisci help maintain the structural integrity of the knee. However, the poor healing potential of the meniscus following a knee injury can not only end a career in sports but lead to osteoarthritis later in life. Complete understanding of meniscal structure is essential for evaluating its risk for injury and subsequent successful repair. This study used novel approaches to elucidate meniscal architecture. The radial and circumferential collagen fibrils in the meniscus were investigated using novel tissue-preparative techniques for light and electron microscopic studies. The results demonstrate a unique architecture based on differences in the packaging of the fundamental collagen fibrils. For radial arrays, the collagen fibrils are arranged in parallel into ∼10 μm bundles, which associate laterally to form flat sheets of varying dimensions that bifurcate and come together to form a honeycomb network within the body of the meniscus. In contrast, the circumferential arrays display a complex network of collagen fibrils arranged into ∼5 μm bundles. Interestingly, both types of architectural organization of collagen fibrils in meniscus are conserved across mammalian species and are age and sex independent. These findings imply that disruptions in meniscal architecture following an injury contribute to poor prognosis for functional repair.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".