Quantitative T2 Mapping of Knee Cartilage: Differentiation of Healthy Control Cartilage and Cartilage Repair Tissue in the Knee with Unloading—Initial Results
Bibliographic record
Abstract
PURPOSE: To prospectively determine on T2 cartilage maps the effect of unloading during a clinical magnetic resonance (MR) examination in the postoperative follow-up of patients after matrix-associated autologous chondrocyte transplantation (MACT) of the knee joint. MATERIALS AND METHODS: Ethical approval for this study was provided by the local ethics commission, and written informed consent was obtained. Thirty patients (mean age, 35.4 years +/- 10.5) with a mean postoperative follow-up period of 29.1 months +/- 24.4 were enrolled. A multiecho spin-echo T2-weighted sequence was performed at the beginning (early unloading) and end (late unloading) of the MR examination, with an interval of 45 minutes. Mean and zonal region of interest T2 measurements were obtained in control cartilage and cartilage repair tissue. Statistical analysis of variance was performed. RESULTS: The change in T2 values of control cartilage (early unloading, 50.2 msec +/- 8.4; late unloading, 51.3 msec +/- 8.5) was less pronounced than the change in T2 values of cartilage repair tissue (early unloading, 51.8 msec +/- 11.7; late unloading, 56.1 msec +/- 14.4) (P = .024). The difference between control cartilage and cartilage repair tissue was not significant for early unloading (P = .314) but was significant for late unloading (P = .036). Zonal T2 measurements revealed a higher dependency on unloading for the superficial cartilage layer. CONCLUSION: Our results suggest that T2 relaxation can be used to assess early and late unloading values of articular cartilage in a clinical setting and that the time point of the quantitative T2 measurement affects the differentiation between native and abnormal articular cartilage. (c) RSNA, 2010.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".