Healing ligament mechanical properties are improved by repair with interpositional allografts but not by concomitant treatment with hyaluronic acid
Bibliographic record
Abstract
Healing ligaments have inferior mechanical properties compared to normal ligaments during early healing intervals. The purpose of this study was to investigate if in vivo ligament repair with an interpositional allograft and treatment with hyaluronic acid (HA) would improve the mechanical properties of a medial collateral ligament (MCL) healing from a gap injury. Twenty rabbits were assigned equally to either a donor or recipient group. A gap injury of the MCL was created in both hindlimbs of 10 recipient animals. The right hindlimb was treated with allograft plus HA while the left hindlimb was treated with allograft only. Low-load and high-load mechanical properties, including laxity, relaxation and failure, and histology were evaluated after 6 weeks of healing. Mechanical results were compared to previously published normal MCL and MCL gap scar data. MCL allografts had greater initial force during cyclic relaxation testing and maximum force during failure testing than MCL scars, but were weaker than normal MCLs. Failure stress was the only parameter to demonstrate a statistically significant effect of treatment with HA on the allografts. However, the failure stress of the HA-treated MCL allografts was not different than MCL scars and was less than normal MCLs. In conclusion, interpositional allografts could enhance some mechanical properties of ligament healing but HA, in the way we applied it, did not produce an obvious improvement.
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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 | 0.000 | 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) | 0.001 | 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".