In vivo biochemical assessment of cartilage with gagCEST MRI: Correlation with cartilage properties
Bibliographic record
Abstract
To assess articular cartilage in vivo, a noninvasive measurement is proposed to evaluate damage of the cartilage. It is hypothesized that glycosaminoglycan chemical exchange saturation transfer (gagCEST) can be applied as a noninvasive imaging technique as it would relate to electromechanical indentation and GAG content as measured with biochemical assays. This pilot study applies gagCEST MRI in total knee arthroplasty (TKA) patients to assess substantially damaged articular cartilage. The outcome was verified against electromechanical indentation and biochemical assays to assess the potential of gagCEST MRI. Five TKA patients were scanned on a 7.0 T MRI with a gagCEST sequence. Articular resurfacing cuts after TKA were obtained for electromechanical and biochemical analyses. The gagCEST MRI measurements on the medial condyle showed a moderate correlation with the GAG content, although sensitivity on the lateral condyle was lacking. Additionally, a strong negative correlation of gagCEST MRI with the electromechanical measurements was observed in the regression analysis. Correlation of gagCEST MRI with electromechanical measurements was shown, but the correlation of gagCEST MRI with GAG content was not convincing. In conclusion, gagCEST could be a useful tool to assess the GAG content in articular cartilage noninvasively, although the mismatch in heterogeneity requires further investigation.
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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) | 0.000 | 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".