Alterations in mechanical properties of rabbit collateral ligaments eight weeks after anterior cruciate ligament transection
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injury is a common knee ligament injury among young, active adults; however, little is known about its impact on the viscoelastic properties of the knee joint's collateral ligaments. This study aimed to characterize and compare the viscoelastic properties of rabbit collateral ligaments in healthy control knees, injured knees, and knees contralateral to the injured knees. Unilateral anterior cruciate ligament transection was performed on six New Zealand white rabbits to create an ACL injury model. Medial and lateral collateral ligaments (MCL and LCL) were collected from the injured and contralateral knees eight weeks after ACL transection. Ligaments were also harvested from both knees of four unoperated rabbits. The ligaments underwent tensile stress-relaxation testing at strain levels of 2, 4, 6, and 8 %, and a sinusoidal loading test at 8 % strain with 0.5 % strain amplitude using frequencies of 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz. The results showed that collateral ligaments of ACL-transected knees relaxed slower compared to control knees. Sinusoidal testing revealed that contralateral knee LCLs had significantly higher storage and loss modulus across all test frequencies. The results indicate that contralateral knee LCLs become stiffer compared to LCLs from control and ACL-transected knees, while LCLs from ACL-transected knees become less viscous compared to LCLs from control and contralateral knees. This study suggests that knee ligaments undergo adaptations following an ACL injury that may affect the mechanics of the ACL-transected knee, which should be considered in biomechanical and rehabilitation studies of patients with an ACL injury.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".