Decreased posterior cruciate and altered collateral ligament loading following ACL transection: A longitudinal study in the ovine model
Bibliographic record
Abstract
Although ACL deficiency is shown to lead to joint degeneration, few quantitative data are reported on its effect on soft tissue structures surrounding the knee joint, specifically, the posterior cruciate and collateral ligaments. The kinematics of the stifle joint of sheep (N = 5) were measured during "normal" gait, as well as 4 and 20 weeks after ACL transection. These motions were reproduced using a unique robotic manipulator and the loads borne by PCL, MCL, and LCL during gait were determined. Our results demonstrated a significant decrease in mean PCL loads 20 weeks post-ACL injury, at hoof-strike (0% of gait, p = 0.034), hoof-off (66% of gait, p = 0.006), peak-swing (85% of gait, p = 0.026), and extension-before-hoof-strike (95% of gait, p = 0.028). Mean MCL loads did not significantly increase following ACL transection, maybe due to large between-animal variation. Finally, mean LCL loads indicated a significant decrease (p < 0.047) at 20 weeks across the entire gait cycle. From a clinical perspective, the load redistributions observed in cruciate and collateral ligaments following ACL injury indicate that these tissues can carry/adapt to the altered mechanical environment of the joint. The considerable variability in the magnitudes of change following ACL injury among animals also simulates clinical variability in humans after trauma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".