Can MTR be used to assess cartilage in the presence of Gd‐DTPA<sup>2–</sup>?
Bibliographic record
Abstract
Magnetization transfer (MT) and T(1) and T(2) relaxation of normal, trypsinized, and interleukin-1beta (IL-1beta)-treated cartilage were measured in the absence and presence of Gd-DTPA(2-). Without the addition of Gd-DTPA(2-), neither T(1) nor T(2) showed any significant change with cartilage damage. However, with Gd-DTPA(2-), trypsinized cartilage exhibited substantially shorter T(1) than normal cartilage, as expected due to the glycosaminoglycan (GAG) loss in these samples, and associated increased Gd-DTPA(2-) concentration. The T(2) results were similar, but less dramatic. The MT pseudo first-order exchange rate, RM(0B), did not depend on the contrast agent concentration, as expected, and was significantly faster for trypsinized and slower for IL-1beta-treated cartilage. In both cases, the MT fraction of the macromolecular pool M(0B) decreased while only trypsinized cartilage showed an increase in MT exchange rate R. The MT ratio (MTR) decreased with increasing Gd-DTPA(2-) concentration. However, interpretation of the MTR results in the presence of Gd-DTPA(2-) was complicated due to competing effects of increased longitudinal relaxivity and MT exchange. Therefore, in a cartilage sample with an unknown degree of GAG depletion and some collagen damage, a full MT analysis might be used to probe the molecular state of cartilage, but it would not be possible to use a simple MTR measurement after the administration of Gd-DTPA(2-) to differentially determine the amount of cartilage degradation in the sample.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".