Ex vivo evaluation of the soft tissue components of the equine stifle using 3 Tesla magnetic resonance imaging under flexion, extension, and loading
Bibliographic record
Abstract
High-field MRI of the equine stifle provides high-resolution information about soft tissues that is useful in the diagnosis of stifle lameness. The aim of this prospective anatomic study was to describe the appearance, position, size, and shape of the equine femorotibial ligaments, meniscal ligaments, and menisci using 3 Tesla MRI under extended, extended-loaded, and flexed conditions. Additionally, histologic examination of the collateral and cruciate ligaments (CLs) of a single stifle was performed to compare with MRI images. In extension, mild variations in MRI signal intensity were apparent in the CLs, and the cranial had two distinct longitudinal regions indicating two ligament bundles. Flexion had minor effects on CL signal intensity and altered the tibial angles of attachment. Histology indicated that both CLs were comprised of two fiber bundles. The collateral ligaments were the same low-signal intensity. The medial collateral ligament had a smaller cross-sectional area than the lateral, and flexion increased the length of the medial collateral ligament and the cross-sectional area of the lateral. Low loads in extension did not affect the MRI appearance of stifle soft tissues. Flexion of the stifle impacted cruciate ligament insertion angles and the size and shape of collateral ligaments. This study provides support for the use of MRI to understand the anatomy and function of stifle ligaments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".