The study of knee tibiofemoral condyle cartilage relaxation characters based on quantitative MR T2 imaging
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative joint disease that leads to the articular cartilage (AC) degeneration and joint function loss. Early stage OA is primarily associated with proteoglycan (PG) loss and collagen structure changes. The MR T2 imaging is a promising non-invasive diagnostic tool that has shown the potential to reflect changes in the biochemical composition of cartilage with early OA. T2 relaxation times give a quantitative measure of the molecular interactions occurring within the imaged cartilage tissues. It can represent cartilage tissue biochemical character that can be quantified with the help of specific imaging strategies. The goal of this study was to apply Levenberg-Marquardt curve fitting algorithm for T2 mapping quantification and T2 relaxation time calculation to study T2 relaxation characters based on quantitative MR T2 imaging of the tibiofemoral condyle cartilage. The MR T2 images of a healthy male volunteer's right knee were generated by a 3T MRI scanner using a spin echo multislice multiecho (MSME) Carr-Purcell Meiboom-Gill (CPMG) sequence. The medial and lateral tibiofemoral condyle cartilage was further subdivided into the regions of interest (ROIs) for identifying variation in T2 values. T2 relaxation times mean and standard deviation of ROIs were calculated using Levenberg-Marquardt curve fitting algorithm with correction and without correction. The results show that the Levenberg-Marquardt curve fitting algorithm was feasible for T2 relaxation time calculation of tibiofemoral cartilage, the CPMG sequence was sensitive to cartilage tissue imaging, and T2 parameter can be used for the characterization of articular cartilage tissue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".