Canine idiopathic epilepsy: prevalence, risk factors and outcome associated with cluster seizures and status epilepticus
Bibliographic record
Abstract
OBJECTIVES: To evaluate the prevalence of cluster seizures and status epilepticus in dogs with idiopathic epilepsy and determine risk factors for cluster seizure frequency, severity and patient outcome. METHODS: Retrospective review of medical records of 407 dogs with idiopathic epilepsy was made. Follow-up questionnaires were evaluated in cases with cluster seizures. RESULTS: Mean age at diagnosis of idiopathic epilepsy was 4 years. Cluster seizures were documented in 169 (41%) dogs. German shepherds and boxers were significantly (P=0·04 and 0·01, respectively) more likely to suffer from cluster seizures compared to Labrador retrievers. There was no association between the occurrence of status epilepticus and cluster seizures and frequency and severity of cluster seizures and status epilepticus episodes with age or breed. Intact males were twice as likely (P=0·003) than neutered dogs to suffer from cluster seizures. Intact females had significantly (P=0·007) more frequent cluster seizures than neutered dogs. The median survival time for all dogs with cluster seizures was 95 months. Significantly (P=0·03) more dogs with frequent cluster seizures were euthanased because of the cluster seizures. CLINICAL SIGNIFICANCE: There was a high prevalence of cluster seizures in dogs with idiopathic epilepsy. Neutering status appears to influence cluster seizure occurrence with intact females more likely to experience more frequent episodes. Euthanasia is associated with frequency of cluster seizure episodes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".