Animal models of osteoarthritis: characterization of a model induced by Mono-Iodo-Acetate injected in rabbits
Bibliographic record
Abstract
Knee Osteoarthritis is a considerable public health concern, both in terms of life quality and treatment financial impacts. To investigate this disease, animal models are deemed a promising alternative. In fact, although a perfect model is generally farfetched, the creation of models that simulate human disease as accurately as possible remains an important research stake. This study aims to highlight the usefulness of the model induced by injected Mono-Iodo-Acetate and to standardize it for the rabbit species. Osteoarthritis was induced by an infra-patellar injection of 0.2 ml of an MIA solution in the left knee of 24 female New Zealand rabbits. The right knee served as a control by receiving an injection of physiological serum. The rabbits were divided into 4 groups of 6 individuals each according to the dose of MIA received per knee. All rabbits were euthanized 30 days after the injection. After sacrifice, the knees were carefully dissected and macroscopic and microscopic scores of cartilage, meniscal and synovial lesions were attributed to each group. Our study followed the laboratory animal care and management guideline published in 2017 by the Canadian Council of Animal Care. The control knees of all rabbits showed no macroscopic or microscopic lesions. The macroscopic lesions: osteophytes, meniscal lesions, fibrillation and erosion of the cartilage and microscopic lesions: disorganization of the chondrocytes, decrease in proteoglycans and synovial inflammation clinically diagnosed in human pathology were all detected and were similarly reproducible among the knees of the same group. Through this work, we highlighted the merits of the arthritis model induced by MIA, namely its simulation of several aspects of human pathology. Further advantages are low cost, speed, reproducibility. This model notably avoids delicate and risky surgical operations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".