FAT-SUPPRESSED SPOILED GRADIENT-RECALLED IMAGING OF EQUINE METACARPOPHALANGEAL ARTICULAR CARTILAGE
Bibliographic record
Abstract
The purpose was to evaluate the capacity of 1.5 T magnetic resonance (MR) imaging to assess articular cartilage in racehorses with naturally occurring metacarpophalangeal joint osteoarthritis. A sagittal, three-dimensional spoiled gradient-recalled echo (SPGR) with fat saturation (FS) sequence was acquired ex vivo on 20 joints. Following joint dissection, specific areas on the third metacarpal condyle were designated for subsequent sampling for histologic cartilage thickness measurement and modified Mankin scoring. Cartilage thickness was measured and cartilage signal intensity was also graded (0-3) on MR images at these selected metacarpal sites. Cartilage structure was graded (0-3) macroscopically and on MR images by two examiners in defined subregions of the proximal phalanx, third metacarpal, and proximal sesamoid bones. There was good precision (mean error 0.11 mm) and moderate correlation (r = 0.44; P < 0.0001) of cartilage thickness measurements between MR images (0.90 +/- 0.17mm) and histology (0.79 +/- 0.16 mm). There was moderate correlation between modified Mankin histologic score and signal intensity of cartilage (r = 0.36; P < 0.01) or MR cartilage structure assessment (r = 0.49, P > 0.001) on SPGR-FS. The sensitivity to detect full-thickness cartilage erosion on MR was only moderate (0.56), and these lesions were often underestimated, particularly when linear in nature. However, the specificity to detect such lesions on MR was high (0.92). While few limitations were identified, the use of a clinically applicable SPGR-FS sequence allows a reasonably accurate method to assess structural changes affecting the articular cartilage of the equine metacarpophalangeal joint.
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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) | 0.001 | 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 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".