Multiparametric MR imaging reveals early cartilage degeneration at 2 and 8 weeks after ACL transection in a rabbit model
Bibliographic record
Abstract
Abstract In this study, the rabbit model with anterior cruciate ligament transection (ACLT) was used to investigate early degenerative changes in cartilage using multiparametric quantitative magnetic resonance imaging (qMRI). ACLT was surgically induced in the knees of skeletally mature New Zealand White rabbits (n = 14). ACL transected and contralateral knee compartments—medial femur, lateral femur, medial tibia, and lateral tibia—were harvested 2 (n = 8) and 8 weeks (n = 6) postsurgery. Twelve age‐matched nonoperated rabbits served as control. qMRI was conducted at 9.4 T and included relaxation times T1, T2, continuous‐wave T1ρ (CWT1ρ), adiabatic T1ρ (AdT1ρ), adiabatic T2ρ (AdT2ρ), and relaxation along a fictitious field (TRAFF). For reference, quantitative histology and biomechanical measurements were carried out. Posttraumatic changes were primarily noted in the superficial half of the cartilage. Prolonged T1, T2, CWT1ρ, and AdT1ρ were observed in the lateral femur 2 and 8 weeks post‐ACLT, compared with the corresponding control and contralateral groups (P < .05). Collagen orientation was significantly altered in the lateral femur at 2 weeks post‐ACLT compared with the corresponding control group. In the medial femur, all the studied relaxation time parameters, except TRAFF, were increased 8 weeks post‐ACLT, as compared with the corresponding contralateral and control groups (P < .05). Similarly, significant proteoglycan loss was observed in the medial femur at 8 weeks following surgery (P < .05). Multiparametric MRI demonstrated early degenerative changes primarily in the superficial cartilage with T1, T2, CWT1ρ, and AdT1ρ sensitive to cartilage changes at 2 weeks after surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".