Healing ligaments have decreased cyclic modulus compared to normal ligaments and immobilization further compromises healing ligament response to cyclic loading
Bibliographic record
Abstract
Ligaments help maintain joint stability by resisting excessive strain during the repetitive loading experienced during daily activity. Healing ligaments may be less able to fulfill this role, straining more under equivalent loading than normal ligaments. We examined the cyclic stress-strain response of normal and healing ligaments to repetitive low loads (<10% of the normal ligament failure strength). Rabbit medial collateral ligaments (MCLs) were surgically gapped in either a unilateral (right MCL; n=23) or bilateral (right and left MCLs; n=17) fashion with immobilization of the right hindlimb in the bilateral group. These MCL scars were allowed to heal for 3, 6, and 14 weeks and were cyclic creep tested at 2.2, 4.1, and 7.1 MPa, respectively. Creep test stresses were a constant 30% of the failure strength of non-immobilized scars at the different healing intervals. Normal MCLs were creep tested at 4.1 and 7.1 MPa (n=13). The cyclic modulus of the non-immobilized scars was less than that of normal ligaments. The percent increase in modulus during cycling was greater for scars than for normal ligaments, likely related to increased viscous dissipation or material inferiorities in scars. Furthermore, immobilization significantly decreased the ability of scars to resist strain, with a majority of immobilized scars failing during repetitive loading. Such failures were preceded by a reduction in cyclic modulus indicating damage to the healing ligaments that was predictive of eventual total failure. The implications of this study are that joints with healing ligaments may have increased strain in joint structures while they are under stress, potentially leading to joint instability. Although immobilization could be used temporarily to maintain joint stability, remobilization would likely lead to total failure of the healing ligament.
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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 | 0.001 |
| 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) | 0.002 | 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".