Relationship between magnetization transfer ratio and axial compressive strain in tibiofemoral articular cartilage
Bibliographic record
Abstract
The objective of this study was to determine the relationship between magnetization transfer ratio (MTR) and strain in ex vivo bovine cartilage using 9.4T magnetic resonance imaging (MRI) and to compare this relationship to the relationship between T2 and strain. A previously designed custom electropneumatic loading device was used to compress together osteochondral blocks of bovine femoral and tibial cartilage and bone within a 9.4T Bruker MRI scanner. Stepwise loads were applied to compress cartilage to targets of 10%, 20% and 30% strain. Images were acquired for unloaded cartilage and after each load had been held constant for 20 min to minimize creep effects. A custom algorithm was used to quantify T2 and MTR (average, by depth, and column wise) in the region of contact, and to calculate axial cartilage strain. Repeated measures correlation was used to investigate potential correlations between MTR and strain, and T2 and strain. Mean MTR vs strain correlations were r rm = −0.90 (CI = −0.96 to −0.75) in the tibia and r rm = −0.70 (CI = −0.87 to −0.35) in the femur. Mean T2 vs strain correlations were r rm = −0.84 (CI = −0.94 to −0.59) in the tibia and r rm = −0.11 (CI = −0.57 to 0.41) in the femur. Column-wise analyses produced negligible or weak correlations (−0.07 to −0.34 for T2 and MTR). MTR increases with strain in the region of contact, and MTR is more strongly correlated to strain than T2. MTR shows promise as a surrogate measure of strain, especially when averaged over the region of contact. • In loaded cartilage, magnetization transfer ratio increases with strain in the region of contact. • Magnetization transfer ratio is more strongly correlated to strain than T2. • Magnetization transfer ratio shows promise as a surrogate measure of cartilage strain, but further investigation is required.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".