Posttraumatic Osteoarthritis Development and Progression in an Ovine Model of Partial Anterior Cruciate Ligament Transection and Effect of Repeated Intra-articular Methylprednisolone Acetate Injections on Early Disease
Bibliographic record
Abstract
BACKGROUND: Partial anterior cruciate ligament (p-ACL) ruptures are a common injury of athletes. However, few preclinical models have investigated the natural history and treatment of p-ACL injuries. PURPOSE: To (1) demonstrate whether a controlled p-ACL injury model (anteromedial band transection) develops progressive gross morphological and histological posttraumatic osteoarthritis (PTOA)-like changes at 20 and 40 weeks after the injury and (2) investigate the efficacy of repeated (0, 5, 10, and 15 weeks) intra-articular injections of methylprednisolone acetate (MPA; 80 mg/mL) in the mitigation of potential PTOA-like changes after p-ACL transection. STUDY DESIGN: Controlled laboratory study. METHODS: Twenty-one 3- to 5-year-old female Suffolk-cross sheep were allocated to 4 groups: (1) nonoperative controls (n = 5), (2) 20 weeks after p-ACL transection (n = 5), (3) 40 weeks after p-ACL transection (n = 6), and (4) 20 weeks after p-ACL transection + MPA (n = 5). Gross morphological grading and histological analyses were conducted. mRNA expression levels for inflammatory, degradative, and structural molecules were assessed. RESULTS: p-ACL transection led to significantly more combined gross damage ( P = .008) and significant aggregate histological damage ( P = .009) at 40 weeks after p-ACL transection than the nonoperative controls, and damage was progressive over time. Macroscopically, MPA appeared to slightly mitigate gross damage at 20 weeks after p-ACL transection in some animals. However, microscopic analysis revealed that repeated MPA injections after p-ACL transection led to significant loss in proteoglycan content compared with the nonoperative controls and 20 weeks after p-ACL transection ( P = .008 and P = .008, respectively). CONCLUSION: p-ACL transection led to significant gross and histological damage by 40 weeks, which was progressive over time. Multiple repeated MPA injections were not appropriate to mitigate injury-related damage in a p-ACL transection ovine model as significant proteoglycan loss was observed in MPA-treated knees. CLINICAL RELEVANCE: A p-ACL injury leads to slow and progressive PTOA-like joint damage, and multiple repeated injections of glucocorticoids may be detrimental to the knee joint in the long term.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".