Recurrent seizures in cats: Diagnostic approach – when is it idiopathic epilepsy?
Bibliographic record
Abstract
Practical relevance: Seizures are one of the most common neurologic problems recognized in cats, affecting approximately 1-3% of the general population. Treatment options and prognosis are closely related to the underlying cause, so it is important that veterinarians are familiar with the diagnostic approach to cats with seizures and options for medical management. Series outline: This is the first of a two-part article series that reviews the diagnosis and treatment of seizures in cats. Part 1 outlines the classification and terminology used to describe epilepsy and epileptic seizures in cats, and discusses some of the most common and unique causes of recurrent seizures in cats. The diagnostic approach to cats with recurrent seizures is addressed, as are criteria for the diagnosis of idiopathic epilepsy. AUDIENCE: This review of recurrent seizures in cats is intended for all veterinarians who are facing the challenges of seizure diagnosis and management in the feline patient. Evidence base: Recommendations for diagnosis and management of feline seizure disorders have historically been extrapolated from the canine and human literature. The information and guidance provided in this two-part series is based on a review of the recent published literature addressing seizure disorders and antiepileptic treatment in cats, as well as the authors' clinical experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".