THE USE OF MAGNETIC RESONANCE IMAGING IN EVALUATING HORSES WITH SPINAL ATAXIA
Bibliographic record
Abstract
To determine the accuracy of magnetic resonance imaging for diagnosing cervical stenotic myelopathy in horses, 39 horses with spinal ataxia and 20 control horses underwent clinical and neurologic examinations, cervical radiographs, euthanasia, magnetic resonance (MR) imaging of the cervical spine and necropsy. Twenty-four horses were diagnosed with cervical stenotic myelopathy, 5 with cervical vertebral stenosis, 7 with idiopathic ataxia, 3 horses had other causes of ataxia, and 20 were controls. The MR images were assessed for spinal cord intensity changes, presence of spinal cord compression, spinal cord compression direction, shape of spinal cord, and the presence of synovial cysts, joint mice, and degenerative joint disease. The height, width, and area of the spinal cord, dural tube and vertebral canal were measured. The identification of spinal cord compression on MR images was significantly different in horses with cervical stenotic myelopathy (P < 0.02), but in the cervical stenotic myelopathy group the identification of spinal cord compression on MR images had poor to slight agreement with histopathologic evidence of compression (κ = 0.05). Horses with cervical stenotic myelopathy were more likely to have a T2 hyperintensity in the spinal cord (P < 0.05). Horses with cervical stenotic myelopathy or cervical vertebral stenosis were more likely to have degenerative joint disease than control horses or horses with other or idiopathic ataxia.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".