T2 Mapping of Articular Cartilage of Glenohumeral Joint with Routine MRI Correlation—Initial Experience
Bibliographic record
Abstract
The evaluation of articular cartilage currently relies primarily on the identification of morphological alterations of the articular cartilage. Unlike anatomic imaging, T2 mapping is sensitive to changes in the chemical composition and structure of the cartilage. Clinical evaluation of T2 mapping of the glenohumeral joint has not been previously reported. The objectives of this study were to evaluate the feasibility of magnetic resonance T2 mapping of the glenohumeral joint in routine clinical imaging, to assess the normal T2 mapping appearance of the glenohumeral joint, and to compare the findings on T2 maps to conventional MR pulse sequences. Magnetic resonance imaging (MRI) examinations of 27 shoulders were performed in a routine clinical setting. All studies included acquisition of T2 mapping using a dedicated software. The T2 maps were analyzed along with the routine MR exam and correlation of cartilage appearance on T2 map and on conventional MR sequences. T2 imaging maps were obtained successfully in all patients. T2 maps and routine MRI correlated in cases of normal cartilage and prolonged T2 values and cartilage defects. In four cases, increased T2 relaxation times in the cartilage and cartilage defects were more apparent on T2 maps. Acquisition of T2 maps at the time of routine MRI scanning is feasible and not time-consuming.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".