High-field MRI findings in epileptic dogs with a normal inter-ictal neurological examination
Bibliographic record
Abstract
Introduction Epilepsy is one of the most common chronic neurological conditions affecting dogs. Previous research exploring the likelihood of a structural cause of epilepsy specifically in dogs with a normal inter-ictal examination is limited to a small population of dogs using low-field MRI. The aims of this study were to establish high-field (1.0T and 1.5T) MRI findings in dogs presenting with epileptic seizures and a normal inter-ictal examination. Methods Medical records were retrospectively searched for dogs presenting with at least two epileptic seizure events more than 24 h apart. To be included in the study, patients had to have a normal neurological examination, high-field MRI of the brain and have had metabolic and toxic causes excluded. Results Four hundred and twelve dogs were eligible for inclusion. Crossbreeds were most commonly affected (n = 63, 15.3%) followed by Border collies (n = 39, 9.5%) and Labrador retrievers (n = 26, 6.3%). Seventy-six dogs (18.5%) had abnormalities detected on MRI, 60 (78.9%) of which were considered to be incidental. Overall, 16 dogs (3.9%) had a structural cause of their epileptic seizures including neoplasia (n = 13, 81.3%), anomalous (n = 2, 12.5%) and meningoencephalitis of unknown origin (MUO) (n = 1, 6.3%). When split into age group at first epileptic seizure structural lesions were documented in 0/66 dogs aged <1 year, 4/256 (1.6%) dogs aged ≥1 year ≤6 years (three neoplastic and one anomalous), 3/51 (5.9%) aged >6 years ≤8 years (two neoplastic and one MUO), and 9/39 (23.1%) dogs aged >8 years (eight neoplastic, one anomalous). Multivariate analysis identified two risk factors for structural disease: increasing age at first epileptic seizure (p < 0.001, OR = 4.390, CI 2.338–8.072) and a history of status epilepticus (p = 0.049, OR = 4.389, CI 1.010–19.078). Discussion Structural lesions are an uncommon cause of epilepsy at any age in dogs with a normal inter-ictal examination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".