COMPARISON OF MAGNETIC RESONANCE IMAGING AND MYELOGRAPHY IN 18 DOBERMAN PINSCHER DOGS WITH CERVICAL SPONDYLOMYELOPATHY
Bibliographic record
Abstract
Eighteen Doberman pinscher dogs with clinical signs of cervical spondylomyelopathy (wobbler syndrome) underwent cervical myelography and magnetic resonance (MR) imaging. Cervical myelography was performed using iohexol, followed by lateral and ventrodorsal radiographs. Traction myelography was performed using a cervical harness exerting 9 kg of linear traction. MR imaging was performed in sagittal, transverse, and dorsal planes using a 1.5 T magnet with the spine in neutral and traction positions. Three reviewers independently evaluated the myelographic and MR images to determine the most extensive lesion and whether the lesion was static or dynamic. All reviewers agreed with the location of the most extensive lesion on MR images (100%), while the agreement using myelography was 83%. The myelogram and MR imaging findings agreed in the identification of the affected site in 13-16 dogs depending on the reviewer. MR imaging provided additional information on lesion location because it allowed direct examination of the spinal cord diameter and parenchyma. Spinal cord signal changes were seen in 10 dogs. Depending on the reviewer, two to four dogs had their lesions classified as dynamic on myelography but static on MR images. Myelography markedly underscored the severity of the spinal cord compression in two dogs, and failed to identify the cause of the signs in another. The results of this study indicated that, although myelography can identify the location of the lesion in most patients, MR imaging appears to be more accurate in predicting the site, severity, and nature of the spinal cord compression.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".