Correlation of healing capacity with vascular response in the anterior cruciate and medial collateral ligaments of the rabbit
Bibliographic record
Abstract
In clinical terms, functional recovery after anterior cruciate ligament (ACL) injury is generally poorer than after medial collateral ligament (MCL) injury. In experimental studies of injury, the early phases of ligament healing require an augmented blood supply. We hypothesized that the differences in healing properties of the ACL and MCL would be reflected in the magnitude of their vascular responses to partial injury. This study is the first to quantify and define the time course of changes in blood flow and vascular volume following hemisection of the rabbit ACL and MCL. Adult female rabbits were assigned to control, sham operation, ACL hemisection or MCL hemisection groups. Standardized ACL or MCL injuries were surgically induced. About 2, 6 or 16 weeks later, blood flow and vascular volume of the ACL and MCL were measured. The MCL of the rabbit responded to hemisection with a large significant increase in blood flow and a substantial angiogenic response associated with inflammation and scar formation. During subsequent matrix remodelling, blood flow and vascular volume returned towards control values. In contrast, the ACL showed only a 2-fold increase in vascular volume, no increase in blood flow and atrophied after hemisection. The superior capacity of the MCL to increase its blood supply through angiogenesis and increased flow is essential for ligament healing to occur, and may be the major difference in healing potential between the ACL and MCL.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".