Destabilization of the Medial Meniscus and Cartilage Scratch Murine Model of Accelerated Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis is the most prevalent musculoskeletal disease in people over 45, leading to an increasing economic and societal cost. Animal models are used to mimic many aspects of the disease. The present protocol describes the destabilization and cartilage scratch model (DCS) of post-traumatic osteoarthritis. Based on the widely used destabilization of the medial meniscus (DMM) model, DCS introduces three scratches on the cartilage surface. The current article highlights the steps to destabilize the knee by transecting the medial meniscotibial ligament followed by three intentional superficial scratches on the articular cartilage. The possible analysis methods by dynamic weight-bearing, microcomputed tomography, and histology are also demonstrated. While the DCS model is not recommended for studies that focus on the effect of osteoarthritis on the cartilage, it enables the study of osteoarthritis development in a shorter time window, with special focus on (1) osteophyte formation, (2) osteoarthritic and injury pain, and (3) the effect of cartilage damage in the whole joint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".