Meniscus Induced Cartilaginous Damage and Non-linear Gross Anatomical Progression of Early-stage Osteoarthritis in a Canine Model
Bibliographic record
Abstract
BACKGROUND: The predictable outcome of the anterior cruciate ligament transection (ACLT) canine model, and the similarity to naturally occurring osteoarthritis (OA) in humans, provide a translatable method for studying OA. Still, evidence of direct meniscus-induced cartilaginous damage has not been identified, and gross-anatomical blinded scoring of early-stage OA has not been performed. OBJECTIVE: A gross anatomical observation and statistical analysis of OA progression to determine meniscus induced cartilaginous damage, to measure the macroscopic progression of OA, and to address matters involving arthroscopic and surgical procedures of the knee. METHOD: Unblinded assessment and blinded scoring of meniscal, tibial, femoral, and patellar damage were performed for control and at four time points following unilateral ACLT: 3-week (N=4), 8-week (N=4), 12-week (N=5), and 25-week (N=4). Mixed-model statistics illustrates damage (score) progression; Wilcoxon rank-sum tests compared time-point scores; and Wilcoxon signed-rank tests compared ACLT and contralateral scores, and meniscus and tibia scores. RESULT: Damage was manifest first on the posterior aspect of the medial meniscus and subsequently on the tibia and femur, implying meniscal damage can precede, coincide with, and aggravate cartilage damage. Damage extent varied chronologically and was dependent upon the joint component. Meniscal damage was evident at 3 weeks and progressed through 25-weeks. Meniscal loose bodies corresponded to tibial cartilage damage location and extent through 12 weeks, followed by cartilage repair activity after complete meniscal degeneration. CONCLUSION: This study provides additional information for understanding OA progression, identifying OA biomarkers, and arthroscopic and meniscectomy procedures.
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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) | 0.001 | 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 | 0.001 |
| 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".