Periarticular ligament changes following ACL/MCL transection in an ovine stifle joint model of osteoarthritis
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injuries often lead to significant functional impairment, and are associated with increased risk for induction of degenerative joint disease. However, few studies have described the effect of ligament transection on the remaining intact knee ligaments. This study sought to determine specifically what impact combined ACL/medial collateral ligament (MCL) transection had on the remaining intact knee ligaments, particularly from the histological, biochemical, and molecular perspectives. Twenty weeks post-ACL/MCL transection, the cut ends of sheep MCLs were bridged by scar, while the posterior cruciate ligaments (PCLs) and lateral collateral ligaments (LCLs) seemed gross morphologically normal. Water content and cell density increased significantly in the MCL scars and the intact PCLs but were unchanged in the LCLs. Collagen fibril diameter distribution was significantly altered in both MCL scar tissue and uninjured PCLs from transected joints. MMP-13 mRNA levels in MCL scars and PCLs from ligament transected joints were increased, while TIMP-1 mRNA levels were significantly decreased in the PCLs only. This study has shown that some intact ligaments in injured joints are impacted by the injury. The joint appears to behave like an integrated organ system, with injury to one component affecting the other components as the "organ" attempts to adapt to the loss of integrity.
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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 | 0.000 |
| 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".