Ligament creep recruits fibres at low stresses and can lead to modulus‐reducing fibre damage at higher creep stresses: a study in rabbit medial collateral ligament model
Bibliographic record
Abstract
Ligaments are subjected to a range of loads during different activities in vivo, suggesting that they must resist creep at various stresses. Cyclic and static creep tests of rabbit medial collateral ligament were used as a model to examine creep over a range of stresses in the toe- and linear-regions of the stress-strain curve: 4.1 MPa (n = 7), 7.1 MPa (n = 6), 14 MPa (n = 9) and 28 MPa (n = 6). We quantified ligament creep behaviour to determine if, at low stresses, modulus would increase in a cyclic creep test and collagen fibres would be recruited in a static creep test. At higher creep stresses, a decrease in measured modulus was expected to be a potential marker of damage. The increase in modulus during cyclic creep and the increase in strain during static creep were similar between the three toe-region stresses (4.1, 7.1, 14 MPa). However, at the linear-region stress (28 MPa), both these parameters increased significantly compared to the increases at the three toe-region stresses. A concurrent crimp analysis revealed that collagen fibres were recruited during creep, evidenced by decreased area of crimped fibres at the end of the static creep test. Interestingly, a predominance of straightened fibres was observed at the end of the 28 MPa creep test, suggesting a limited potential for fibre recruitment at higher, linear-region stresses. An additional 28 MPa (n = 6) group had mechanically detectable discontinuities in their stress-strain curves during creep that were related to reductions in modulus and suggested fibre damage. These data support the concept that collagen fibre recruitment is a mechanism by which ligaments resist creep at low stresses. At a higher creep stress, which was still only about a third of the failure capacity, damage to some ligaments occurred and was marked by a sudden reduction in modulus. In the cyclic tests, with continued cycling, the modulus increased back to original values obtained before the discontinuity suggesting that other fibres were being recruited to bear load. These results have important implications for our understanding of how fibre recruitment and stress redistribution act in normal ligament to minimize creep and restore modulus after fibre damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".